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Toni-SM/skrl/docs/source/examples/isaacgym/torch_cartpole_ppo.py | import isaacgym
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_isaacgym_env_preview4
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(40)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 32),
nn.ELU(),
nn.Linear(32, 32),
nn.ELU())
self.mean_layer = nn.Linear(32, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(32, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Isaac Gym environment
env = load_isaacgym_env_preview4(task_name="Cartpole")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 1 # 16 * 512 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 16
cfg["experiment"]["checkpoint_interval"] = 80
cfg["experiment"]["directory"] = "runs/torch/Cartpole"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 1600, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/IsaacGymEnvs-Cartpole-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 4,936 | Python | 36.976923 | 101 | 0.678485 |
Toni-SM/skrl/docs/source/examples/isaacgym/torch_quadcopter_ppo.py | import isaacgym
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_isaacgym_env_preview4
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU())
self.mean_layer = nn.Linear(128, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(128, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Isaac Gym environment
env = load_isaacgym_env_preview4(task_name="Quadcopter")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=8, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 8 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 4 # 8 * 8192 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 20
cfg["experiment"]["checkpoint_interval"] = 200
cfg["experiment"]["directory"] = "runs/torch/Quadcopter"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 4000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/IsaacGymEnvs-Quadcopter-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,044 | Python | 37.219697 | 101 | 0.669905 |
Toni-SM/skrl/docs/source/examples/isaacgym/jax_factory_task_nut_bolt_pick_ppo.py | import isaacgym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_isaacgym_env_preview4
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Isaac Gym environment
env = load_isaacgym_env_preview4(task_name="FactoryTaskNutBoltPick")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=120, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 120 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 30 # 120 * 128 / 512
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-4
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0.016
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 614
cfg["experiment"]["checkpoint_interval"] = 6144
cfg["experiment"]["directory"] = "runs/jax/FactoryTaskNutBoltPick"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 122880, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPick-PPO", filename="agent.pickle")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,004 | Python | 36.074074 | 113 | 0.689249 |
Toni-SM/skrl/docs/source/examples/isaacgym/torch_factory_task_nut_bolt_place_ppo.py | import isaacgym
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_isaacgym_env_preview4
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU())
self.mean_layer = nn.Linear(64, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(64, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Isaac Gym environment
env = load_isaacgym_env_preview4(task_name="FactoryTaskNutBoltPlace")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=120, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 120 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 30 # 120 * 128 / 512
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-4
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0.016
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 614
cfg["experiment"]["checkpoint_interval"] = 6144
cfg["experiment"]["directory"] = "runs/torch/FactoryTaskNutBoltPlace"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 122880, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 4,934 | Python | 36.961538 | 110 | 0.670653 |
Toni-SM/skrl/docs/source/examples/isaacgym/torch_factory_task_nut_bolt_screw_ppo.py | import isaacgym
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_isaacgym_env_preview4
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU())
self.mean_layer = nn.Linear(64, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(64, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Isaac Gym environment
env = load_isaacgym_env_preview4(task_name="FactoryTaskNutBoltScrew")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=128, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 128 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 32 # 128 * 128 / 512
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-4
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0.016
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 614
cfg["experiment"]["checkpoint_interval"] = 6144
cfg["experiment"]["directory"] = "runs/torch/FactoryTaskNutBoltScrew"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 122880, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 4,934 | Python | 36.961538 | 110 | 0.670653 |
Toni-SM/skrl/docs/source/examples/deepmind/dm_suite_cartpole_swingup_ddpg.py | from dm_control import suite
import torch
import torch.nn as nn
import torch.nn.functional as F
# Import the skrl components to build the RL system
from skrl.models.torch import Model, DeterministicMixin
from skrl.memories.torch import RandomMemory
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.envs.torch import wrap_env
# Define the models (deterministic models) for the DDPG agent using mixins
# and programming with two approaches (torch functional and torch.nn.Sequential class).
# - Actor (policy): takes as input the environment's observation/state and returns an action
# - Critic: takes the state and action as input and provides a value to guide the policy
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
return torch.tanh(self.action_layer(x)), {}
class DeterministicCritic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 400),
nn.ReLU(),
nn.Linear(400, 300),
nn.ReLU(),
nn.Linear(300, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
# Load and wrap the DeepMind environment
env = suite.load(domain_name="cartpole", task_name="swingup")
env = wrap_env(env)
device = env.device
# Instantiate a RandomMemory (without replacement) as experience replay memory
memory = RandomMemory(memory_size=25000, num_envs=env.num_envs, device=device, replacement=False)
# Instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ddpg.html#spaces-and-models
models_ddpg = {}
models_ddpg["policy"] = DeterministicActor(env.observation_space, env.action_space, device, clip_actions=True)
models_ddpg["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device, clip_actions=True)
models_ddpg["critic"] = DeterministicCritic(env.observation_space, env.action_space, device)
models_ddpg["target_critic"] = DeterministicCritic(env.observation_space, env.action_space, device)
# Initialize the models' parameters (weights and biases) using a Gaussian distribution
for model in models_ddpg.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# Configure and instantiate the agent.
# Only modify some of the default configuration, visit its documentation to see all the options
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ddpg.html#configuration-and-hyperparameters
cfg_ddpg = DDPG_DEFAULT_CONFIG.copy()
cfg_ddpg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg_ddpg["batch_size"] = 100
cfg_ddpg["random_timesteps"] = 100
cfg_ddpg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints each 1000 and 5000 timesteps respectively
cfg_ddpg["experiment"]["write_interval"] = 1000
cfg_ddpg["experiment"]["checkpoint_interval"] = 5000
agent_ddpg = DDPG(models=models_ddpg,
memory=memory,
cfg=cfg_ddpg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# Configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 50000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent_ddpg)
# start training
trainer.train()
| 4,425 | Python | 43.26 | 117 | 0.712542 |
Toni-SM/skrl/docs/source/examples/deepmind/dm_manipulation_stack_sac.py | from dm_control import manipulation
import torch
import torch.nn as nn
# Import the skrl components to build the RL system
from skrl.models.torch import Model, GaussianMixin, DeterministicMixin
from skrl.memories.torch import RandomMemory
from skrl.agents.torch.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.trainers.torch import SequentialTrainer
from skrl.envs.torch import wrap_env
# Define the models (stochastic and deterministic models) for the SAC agent using the mixins.
# - StochasticActor (policy): takes as input the environment's observation/state and returns an action
# - Critic: takes the state and action as input and provides a value to guide the policy
class StochasticActor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std)
self.features_extractor = nn.Sequential(nn.Conv2d(3, 32, kernel_size=8, stride=3),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=2, stride=1),
nn.ReLU(),
nn.Flatten(),
nn.Linear(7744, 512),
nn.ReLU(),
nn.Linear(512, 8),
nn.Tanh())
self.net = nn.Sequential(nn.Linear(26, 32),
nn.ReLU(),
nn.Linear(32, 32),
nn.ReLU(),
nn.Linear(32, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
states = inputs["states"]
# The dm_control.manipulation tasks have as observation/state spec a `collections.OrderedDict` object as follows:
# OrderedDict([('front_close', BoundedArray(shape=(1, 84, 84, 3), dtype=dtype('uint8'), name='front_close', minimum=0, maximum=255)),
# ('jaco_arm/joints_pos', Array(shape=(1, 6, 2), dtype=dtype('float64'), name='jaco_arm/joints_pos')),
# ('jaco_arm/joints_torque', Array(shape=(1, 6), dtype=dtype('float64'), name='jaco_arm/joints_torque')),
# ('jaco_arm/joints_vel', Array(shape=(1, 6), dtype=dtype('float64'), name='jaco_arm/joints_vel')),
# ('jaco_arm/jaco_hand/joints_pos', Array(shape=(1, 3), dtype=dtype('float64'), name='jaco_arm/jaco_hand/joints_pos')),
# ('jaco_arm/jaco_hand/joints_vel', Array(shape=(1, 3), dtype=dtype('float64'), name='jaco_arm/jaco_hand/joints_vel')),
# ('jaco_arm/jaco_hand/pinch_site_pos', Array(shape=(1, 3), dtype=dtype('float64'), name='jaco_arm/jaco_hand/pinch_site_pos')),
# ('jaco_arm/jaco_hand/pinch_site_rmat', Array(shape=(1, 9), dtype=dtype('float64'), name='jaco_arm/jaco_hand/pinch_site_rmat'))])
# This spec is converted to a `gym.spaces.Dict` space by the `wrap_env` function as follows:
# Dict(front_close: Box(0, 255, (1, 84, 84, 3), uint8),
# jaco_arm/jaco_hand/joints_pos: Box(-inf, inf, (1, 3), float64),
# jaco_arm/jaco_hand/joints_vel: Box(-inf, inf, (1, 3), float64),
# jaco_arm/jaco_hand/pinch_site_pos: Box(-inf, inf, (1, 3), float64),
# jaco_arm/jaco_hand/pinch_site_rmat: Box(-inf, inf, (1, 9), float64),
# jaco_arm/joints_pos: Box(-inf, inf, (1, 6, 2), float64),
# jaco_arm/joints_torque: Box(-inf, inf, (1, 6), float64),
# jaco_arm/joints_vel: Box(-inf, inf, (1, 6), float64))
# The `spaces` parameter is a flat tensor of the flattened observation/state space with shape (batch_size, size_of_flat_space).
# Using the model's method `tensor_to_space` we can convert the flattened tensor to the original space.
# https://skrl.readthedocs.io/en/latest/modules/skrl.models.base_class.html#skrl.models.torch.base.Model.tensor_to_space
space = self.tensor_to_space(states, self.observation_space)
# For this case, the `space` variable is a Python dictionary with the following structure and shapes:
# {'front_close': torch.Tensor(shape=[batch_size, 1, 84, 84, 3], dtype=torch.float32),
# 'jaco_arm/jaco_hand/joints_pos': torch.Tensor(shape=[batch_size, 1, 3], dtype=torch.float32)
# 'jaco_arm/jaco_hand/joints_vel': torch.Tensor(shape=[batch_size, 1, 3], dtype=torch.float32)
# 'jaco_arm/jaco_hand/pinch_site_pos': torch.Tensor(shape=[batch_size, 1, 3], dtype=torch.float32)
# 'jaco_arm/jaco_hand/pinch_site_rmat': torch.Tensor(shape=[batch_size, 1, 9], dtype=torch.float32)
# 'jaco_arm/joints_pos': torch.Tensor(shape=[batch_size, 1, 6, 2], dtype=torch.float32)
# 'jaco_arm/joints_torque': torch.Tensor(shape=[batch_size, 1, 6], dtype=torch.float32)
# 'jaco_arm/joints_vel': torch.Tensor(shape=[batch_size, 1, 6], dtype=torch.float32)}
# permute and normalize the images (samples, width, height, channels) -> (samples, channels, width, height)
features = self.features_extractor(space['front_close'][:,0].permute(0, 3, 1, 2) / 255.0)
mean_actions = torch.tanh(self.net(torch.cat([features,
space["jaco_arm/joints_pos"].view(states.shape[0], -1),
space["jaco_arm/joints_vel"].view(states.shape[0], -1)], dim=-1)))
return mean_actions, self.log_std_parameter, {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.features_extractor = nn.Sequential(nn.Conv2d(3, 32, kernel_size=8, stride=3),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=2, stride=1),
nn.ReLU(),
nn.Flatten(),
nn.Linear(7744, 512),
nn.ReLU(),
nn.Linear(512, 8),
nn.Tanh())
self.net = nn.Sequential(nn.Linear(26 + self.num_actions, 32),
nn.ReLU(),
nn.Linear(32, 32),
nn.ReLU(),
nn.Linear(32, 1))
def compute(self, inputs, role):
states = inputs["states"]
# map the observations/states to the original space.
# See the explanation above (StochasticActor.compute)
space = self.tensor_to_space(states, self.observation_space)
# permute and normalize the images (samples, width, height, channels) -> (samples, channels, width, height)
features = self.features_extractor(space['front_close'][:,0].permute(0, 3, 1, 2) / 255.0)
return self.net(torch.cat([features,
space["jaco_arm/joints_pos"].view(states.shape[0], -1),
space["jaco_arm/joints_vel"].view(states.shape[0], -1),
inputs["taken_actions"]], dim=-1)), {}
# Load and wrap the DeepMind environment
env = manipulation.load("reach_site_vision")
env = wrap_env(env)
device = env.device
# Instantiate a RandomMemory (without replacement) as experience replay memory
memory = RandomMemory(memory_size=50000, num_envs=env.num_envs, device=device, replacement=False)
# Instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.sac.html#spaces-and-models
models_sac = {}
models_sac["policy"] = StochasticActor(env.observation_space, env.action_space, device, clip_actions=True)
models_sac["critic_1"] = Critic(env.observation_space, env.action_space, device)
models_sac["critic_2"] = Critic(env.observation_space, env.action_space, device)
models_sac["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models_sac["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# Initialize the models' parameters (weights and biases) using a Gaussian distribution
for model in models_sac.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# Configure and instantiate the agent.
# Only modify some of the default configuration, visit its documentation to see all the options
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.sac.html#configuration-and-hyperparameters
cfg_sac = SAC_DEFAULT_CONFIG.copy()
cfg_sac["gradient_steps"] = 1
cfg_sac["batch_size"] = 256
cfg_sac["random_timesteps"] = 0
cfg_sac["learning_starts"] = 10000
cfg_sac["learn_entropy"] = True
# logging to TensorBoard and write checkpoints each 1000 and 5000 timesteps respectively
cfg_sac["experiment"]["write_interval"] = 1000
cfg_sac["experiment"]["checkpoint_interval"] = 5000
agent_sac = SAC(models=models_sac,
memory=memory,
cfg=cfg_sac,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# Configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent_sac)
# start training
trainer.train()
| 10,370 | Python | 55.672131 | 151 | 0.579749 |
Toni-SM/skrl/docs/source/examples/robosuite/td3_robosuite_two_arm_lift.py | import robosuite
from robosuite.controllers import load_controller_config
import torch
import torch.nn as nn
import torch.nn.functional as F
# Import the skrl components to build the RL system
from skrl.models.torch import Model, DeterministicMixin
from skrl.memories.torch import RandomMemory
from skrl.agents.torch.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.resources.noises.torch import GaussianNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.envs.torch import wrap_env
# Define the models (deterministic models) for the TD3 agent using mixins
# and programming with two approaches (torch functional and torch.nn.Sequential class).
# - Actor (policy): takes as input the environment's observation/state and returns an action
# - Critic: takes the state and action as input and provides a value to guide the policy
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
return torch.tanh(self.action_layer(x)), {}
class DeterministicCritic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 400),
nn.ReLU(),
nn.Linear(400, 300),
nn.ReLU(),
nn.Linear(300, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
# Load and wrap the DeepMind robosuite environment
controller_config = load_controller_config(default_controller="OSC_POSE")
env = robosuite.make("TwoArmLift",
robots=["Sawyer", "Panda"], # load a Sawyer robot and a Panda robot
gripper_types="default", # use default grippers per robot arm
controller_configs=controller_config, # each arm is controlled using OSC
env_configuration="single-arm-opposed", # (two-arm envs only) arms face each other
has_renderer=True, # on-screen rendering
render_camera="frontview", # visualize the "frontview" camera
has_offscreen_renderer=False, # no off-screen rendering
control_freq=20, # 20 hz control for applied actions
horizon=200, # each episode terminates after 200 steps
use_object_obs=True, # provide object observations to agent
use_camera_obs=False, # don't provide image observations to agent
reward_shaping=True) # use a dense reward signal for learning
env = wrap_env(env)
device = env.device
# Instantiate a RandomMemory (without replacement) as experience replay memory
memory = RandomMemory(memory_size=25000, num_envs=env.num_envs, device=device, replacement=False)
# Instantiate the agent's models (function approximators).
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.td3.html#spaces-and-models
models = {}
models["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["critic_1"] = DeterministicCritic(env.observation_space, env.action_space, device)
models["critic_2"] = DeterministicCritic(env.observation_space, env.action_space, device)
models["target_critic_1"] = DeterministicCritic(env.observation_space, env.action_space, device)
models["target_critic_2"] = DeterministicCritic(env.observation_space, env.action_space, device)
# Initialize the models' parameters (weights and biases) using a Gaussian distribution
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# Configure and instantiate the agent.
# Only modify some of the default configuration, visit its documentation to see all the options
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.td3.html#configuration-and-hyperparameters
cfg_agent = TD3_DEFAULT_CONFIG.copy()
cfg_agent["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg_agent["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg_agent["smooth_regularization_clip"] = 0.5
cfg_agent["batch_size"] = 100
cfg_agent["random_timesteps"] = 100
cfg_agent["learning_starts"] = 100
# logging to TensorBoard and write checkpoints each 1000 and 5000 timesteps respectively
cfg_agent["experiment"]["write_interval"] = 1000
cfg_agent["experiment"]["checkpoint_interval"] = 5000
agent = TD3(models=models,
memory=memory,
cfg=cfg_agent,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# Configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 50000, "headless": False}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,854 | Python | 48.618644 | 104 | 0.67629 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_sac_gru.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.sac import SAC_DEFAULT_CONFIG
from skrl.agents.torch.sac import SAC_RNN as SAC
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Actor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), self.log_std_parameter, {"rnn": [hidden_states]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role in ["target_critic_1", "target_critic_2"] else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg = SAC_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
cfg["learn_entropy"] = True
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = SAC(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 9,935 | Python | 44.577981 | 146 | 0.637947 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_taxi_vector_sarsa.py | import gymnasium as gym
import torch
# import the skrl components to build the RL system
from skrl.agents.torch.sarsa import SARSA, SARSA_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.models.torch import Model, TabularMixin
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define model (tabular model) using mixin
class EpilonGreedyPolicy(TabularMixin, Model):
def __init__(self, observation_space, action_space, device, num_envs=1, epsilon=0.1):
Model.__init__(self, observation_space, action_space, device)
TabularMixin.__init__(self, num_envs)
self.epsilon = epsilon
self.q_table = torch.ones((num_envs, self.num_observations, self.num_actions),
dtype=torch.float32, device=self.device)
def compute(self, inputs, role):
actions = torch.argmax(self.q_table[torch.arange(self.num_envs).view(-1, 1), inputs["states"]],
dim=-1, keepdim=True).view(-1,1)
# choose random actions for exploration according to epsilon
indexes = (torch.rand(inputs["states"].shape[0], device=self.device) < self.epsilon).nonzero().view(-1)
if indexes.numel():
actions[indexes] = torch.randint(self.num_actions, (indexes.numel(), 1), device=self.device)
return actions, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("Taxi-v3", num_envs=10, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Taxi-v")][0]
print("Taxi-v3 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=10, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate the agent's model (table)
# SARSA requires 1 model, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sarsa.html#models
models = {}
models["policy"] = EpilonGreedyPolicy(env.observation_space, env.action_space, device, num_envs=env.num_envs, epsilon=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sarsa.html#configuration-and-hyperparameters
cfg = SARSA_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.999
cfg["alpha"] = 0.4
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1600
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/Taxi"
agent = SARSA(models=models,
memory=None,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 80000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,159 | Python | 37.536585 | 122 | 0.696106 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_taxi_sarsa.py | import gymnasium as gym
import torch
# import the skrl components to build the RL system
from skrl.agents.torch.sarsa import SARSA, SARSA_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.models.torch import Model, TabularMixin
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define model (tabular model) using mixin
class EpilonGreedyPolicy(TabularMixin, Model):
def __init__(self, observation_space, action_space, device, num_envs=1, epsilon=0.1):
Model.__init__(self, observation_space, action_space, device)
TabularMixin.__init__(self, num_envs)
self.epsilon = epsilon
self.q_table = torch.ones((num_envs, self.num_observations, self.num_actions),
dtype=torch.float32, device=self.device)
def compute(self, inputs, role):
actions = torch.argmax(self.q_table[torch.arange(self.num_envs).view(-1, 1), inputs["states"]],
dim=-1, keepdim=True).view(-1,1)
# choose random actions for exploration according to epsilon
indexes = (torch.rand(inputs["states"].shape[0], device=self.device) < self.epsilon).nonzero().view(-1)
if indexes.numel():
actions[indexes] = torch.randint(self.num_actions, (indexes.numel(), 1), device=self.device)
return actions, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("Taxi-v3")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Taxi-v")][0]
print("Taxi-v3 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate the agent's model (table)
# SARSA requires 1 model, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sarsa.html#models
models = {}
models["policy"] = EpilonGreedyPolicy(env.observation_space, env.action_space, device, num_envs=env.num_envs, epsilon=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sarsa.html#configuration-and-hyperparameters
cfg = SARSA_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.999
cfg["alpha"] = 0.4
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1600
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/Taxi"
agent = SARSA(models=models,
memory=None,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 80000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,079 | Python | 36.560975 | 122 | 0.693407 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_ddpg_rnn.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG_DEFAULT_CONFIG
from skrl.agents.torch.ddpg import DDPG_RNN as DDPG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role == "target_policy" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {"rnn": [hidden_states]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role == "target_critic" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 9,822 | Python | 44.476852 | 146 | 0.638974 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_cartpole_vector_dqn.py | import gymnasium as gym
# import the skrl components to build the RL system
from skrl.agents.torch.dqn import DQN, DQN_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
from skrl.utils.model_instantiators.torch import Shape, deterministic_model
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("CartPole-v1", num_envs=5, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("CartPole-v")][0]
print("CartPole-v0 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=5, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=200000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators) using the model instantiator utility.
# DQN requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#models
models = {}
models["q_network"] = deterministic_model(observation_space=env.observation_space,
action_space=env.action_space,
device=device,
clip_actions=False,
input_shape=Shape.OBSERVATIONS,
hiddens=[64, 64],
hidden_activation=["relu", "relu"],
output_shape=Shape.ACTIONS,
output_activation=None,
output_scale=1.0)
models["target_q_network"] = deterministic_model(observation_space=env.observation_space,
action_space=env.action_space,
device=device,
clip_actions=False,
input_shape=Shape.OBSERVATIONS,
hiddens=[64, 64],
hidden_activation=["relu", "relu"],
output_shape=Shape.ACTIONS,
output_activation=None,
output_scale=1.0)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#configuration-and-hyperparameters
cfg = DQN_DEFAULT_CONFIG.copy()
cfg["learning_starts"] = 100
cfg["exploration"]["final_epsilon"] = 0.04
cfg["exploration"]["timesteps"] = 1500
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/CartPole"
agent = DQN(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 50000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,894 | Python | 43.261363 | 98 | 0.59245 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_ppo_gru.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO_DEFAULT_CONFIG
from skrl.agents.torch.ppo import PPO_RNN as PPO
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(rnn_output)), self.log_std_parameter, {"rnn": [hidden_states]}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, 1))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
return self.net(rnn_output), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.vector.make("PendulumNoVel-v1", num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["value"] = Value(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["grad_norm_clip"] = 0.5
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = False
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 0.5
cfg["kl_threshold"] = 0
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 10,058 | Python | 44.107623 | 146 | 0.624279 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulum_vector_ddpg.py | import gymnasium as gym
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("Pendulum-v1", num_envs=10, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=10, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=100000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["batch_size"] = 100
cfg["random_timesteps"] = 100
cfg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 1000
cfg["experiment"]["directory"] = "runs/torch/Pendulum"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,328 | Python | 38.715596 | 106 | 0.715111 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_td3_gru.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.td3 import TD3_DEFAULT_CONFIG
from skrl.agents.torch.td3 import TD3_RNN as TD3
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import GaussianNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role == "target_policy" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {"rnn": [hidden_states]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role in ["target_critic_1", "target_critic_2"] else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 10,106 | Python | 44.940909 | 146 | 0.641599 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_sac_lstm.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.sac import SAC_DEFAULT_CONFIG
from skrl.agents.torch.sac import SAC_RNN as SAC
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Actor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,0,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), self.log_std_parameter, {"rnn": [rnn_states[0], rnn_states[1]]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
sequence_index = 1 if role in ["target_critic_1", "target_critic_2"] else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [rnn_states[0], rnn_states[1]]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg = SAC_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
cfg["learn_entropy"] = True
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = SAC(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 11,201 | Python | 48.131579 | 146 | 0.629408 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_trpo_gru.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.trpo import TRPO_DEFAULT_CONFIG
from skrl.agents.torch.trpo import TRPO_RNN as TRPO
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(rnn_output)), self.log_std_parameter, {"rnn": [hidden_states]}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, 1))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
return self.net(rnn_output), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.vector.make("PendulumNoVel-v1", num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# TRPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["value"] = Value(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#configuration-and-hyperparameters
cfg = TRPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["grad_norm_clip"] = 0.5
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = TRPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 9,735 | Python | 44.495327 | 146 | 0.621263 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_td3_lstm.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.td3 import TD3_DEFAULT_CONFIG
from skrl.agents.torch.td3 import TD3_RNN as TD3
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import GaussianNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
sequence_index = 1 if role == "target_policy" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {"rnn": [rnn_states[0], rnn_states[1]]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
sequence_index = 1 if role in ["target_critic_1", "target_critic_2"] else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [rnn_states[0], rnn_states[1]]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 11,385 | Python | 48.504348 | 146 | 0.633114 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_ppo_lstm.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO_DEFAULT_CONFIG
from skrl.agents.torch.ppo import PPO_RNN as PPO
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,0,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(rnn_output)), self.log_std_parameter, {"rnn": [rnn_states[0], rnn_states[1]]}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, 1))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,0,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
return self.net(rnn_output), {"rnn": [rnn_states[0], rnn_states[1]]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.vector.make("PendulumNoVel-v1", num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["value"] = Value(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["grad_norm_clip"] = 0.5
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = False
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 0.5
cfg["kl_threshold"] = 0
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 11,340 | Python | 47.67382 | 146 | 0.616138 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulum_trpo.py | import gymnasium as gym
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.trpo import TRPO, TRPO_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(inputs["states"])), self.log_std_parameter, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, 1))
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("Pendulum-v1", num_envs=4, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# TRPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True)
models["value"] = Value(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#configuration-and-hyperparameters
cfg = TRPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["grad_norm_clip"] = 0.5
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/Pendulum"
agent = TRPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,467 | Python | 39.252252 | 101 | 0.674726 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_trpo_rnn.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.trpo import TRPO_DEFAULT_CONFIG
from skrl.agents.torch.trpo import TRPO_RNN as TRPO
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(rnn_output)), self.log_std_parameter, {"rnn": [hidden_states]}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, 1))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
return self.net(rnn_output), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.vector.make("PendulumNoVel-v1", num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# TRPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["value"] = Value(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#configuration-and-hyperparameters
cfg = TRPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["grad_norm_clip"] = 0.5
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = TRPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 9,735 | Python | 44.495327 | 146 | 0.621263 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_cartpole_cem.py | import gymnasium as gym
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.cem import CEM, CEM_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import CategoricalMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define model (categorical model) using mixin
class Policy(CategoricalMixin, Model):
def __init__(self, observation_space, action_space, device, unnormalized_log_prob=True):
Model.__init__(self, observation_space, action_space, device)
CategoricalMixin.__init__(self, unnormalized_log_prob)
self.linear_layer_1 = nn.Linear(self.num_observations, 64)
self.linear_layer_2 = nn.Linear(64, 64)
self.output_layer = nn.Linear(64, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
return self.output_layer(x), {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("CartPole-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("CartPole-v")][0]
print("CartPole-v0 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=1000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's model (function approximator).
# CEM requires 1 model, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/cem.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/cem.html#configuration-and-hyperparameters
cfg = CEM_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1000
cfg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/CartPole"
agent = CEM(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,057 | Python | 34.149425 | 96 | 0.72195 |
Toni-SM/skrl/docs/source/examples/gymnasium/jax_gymnasium_pendulum_sac.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Actor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-5, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(400)(inputs["states"]))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
# Pendulum-v1 action_space is -2 to 2
return 2 * nn.tanh(x), log_std, {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(400)(x))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("Pendulum-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, clip_actions=True)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg = SAC_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
cfg["learn_entropy"] = True
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/jax/Pendulum"
agent = SAC(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,473 | Python | 37.239316 | 101 | 0.704002 |
Toni-SM/skrl/docs/source/examples/gymnasium/jax_gymnasium_cartpole_cem.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.cem import CEM, CEM_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import CategoricalMixin, Model
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define model (categorical model) using mixin
class Policy(CategoricalMixin, Model):
def __init__(self, observation_space, action_space, device=None, unnormalized_log_prob=True, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
CategoricalMixin.__init__(self, unnormalized_log_prob)
@nn.compact
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(64)(inputs["states"]))
x = nn.relu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
return x, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("CartPole-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("CartPole-v")][0]
print("CartPole-v0 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=1000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's model (function approximator).
# CEM requires 1 model, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/cem.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/cem.html#configuration-and-hyperparameters
cfg = CEM_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1000
cfg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/jax/CartPole"
agent = CEM(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,087 | Python | 31.851063 | 107 | 0.719469 |
Toni-SM/skrl/docs/source/examples/gymnasium/jax_gymnasium_pendulum_ppo.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(64)(inputs["states"]))
x = nn.relu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
# Pendulum-v1 action_space is -2 to 2
return 2 * nn.tanh(x), log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(64)(inputs["states"]))
x = nn.relu(nn.Dense(64)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("Pendulum-v1", num_envs=4, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["grad_norm_clip"] = 0.5
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = False
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 0.5
cfg["kl_threshold"] = 0
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/jax/Pendulum"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,813 | Python | 37.512 | 101 | 0.706212 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_cartpole_dqn.py | import gymnasium as gym
# import the skrl components to build the RL system
from skrl.agents.torch.dqn import DQN, DQN_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
from skrl.utils.model_instantiators.torch import Shape, deterministic_model
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("CartPole-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("CartPole-v")][0]
print("CartPole-v0 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=50000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators) using the model instantiator utility.
# DQN requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#models
models = {}
models["q_network"] = deterministic_model(observation_space=env.observation_space,
action_space=env.action_space,
device=device,
clip_actions=False,
input_shape=Shape.OBSERVATIONS,
hiddens=[64, 64],
hidden_activation=["relu", "relu"],
output_shape=Shape.ACTIONS,
output_activation=None,
output_scale=1.0)
models["target_q_network"] = deterministic_model(observation_space=env.observation_space,
action_space=env.action_space,
device=device,
clip_actions=False,
input_shape=Shape.OBSERVATIONS,
hiddens=[64, 64],
hidden_activation=["relu", "relu"],
output_shape=Shape.ACTIONS,
output_activation=None,
output_scale=1.0)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#configuration-and-hyperparameters
cfg = DQN_DEFAULT_CONFIG.copy()
cfg["learning_starts"] = 100
cfg["exploration"]["final_epsilon"] = 0.04
cfg["exploration"]["timesteps"] = 1500
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/CartPole"
agent = DQN(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 50000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,815 | Python | 42.363636 | 97 | 0.588204 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_ppo_rnn.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO_DEFAULT_CONFIG
from skrl.agents.torch.ppo import PPO_RNN as PPO
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(rnn_output)), self.log_std_parameter, {"rnn": [hidden_states]}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, 1))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
return self.net(rnn_output), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.vector.make("PendulumNoVel-v1", num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["value"] = Value(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["grad_norm_clip"] = 0.5
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = False
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 0.5
cfg["kl_threshold"] = 0
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 10,058 | Python | 44.107623 | 146 | 0.624279 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_sac.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Actor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), self.log_std_parameter, {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, clip_actions=True)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg = SAC_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
cfg["learn_entropy"] = True
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = SAC(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,585 | Python | 38.534482 | 117 | 0.712105 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_ddpg_lstm.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG_DEFAULT_CONFIG
from skrl.agents.torch.ddpg import DDPG_RNN as DDPG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
sequence_index = 1 if role == "target_policy" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {"rnn": [rnn_states[0], rnn_states[1]]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
sequence_index = 1 if role == "target_critic" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [rnn_states[0], rnn_states[1]]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 11,101 | Python | 48.123894 | 146 | 0.630574 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_frozen_lake_q_learning.py | import gymnasium as gym
import torch
# import the skrl components to build the RL system
from skrl.agents.torch.q_learning import Q_LEARNING, Q_LEARNING_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.models.torch import Model, TabularMixin
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define model (tabular model) using mixin
class EpilonGreedyPolicy(TabularMixin, Model):
def __init__(self, observation_space, action_space, device, num_envs=1, epsilon=0.1):
Model.__init__(self, observation_space, action_space, device)
TabularMixin.__init__(self, num_envs)
self.epsilon = epsilon
self.q_table = torch.ones((num_envs, self.num_observations, self.num_actions),
dtype=torch.float32, device=self.device)
def compute(self, inputs, role):
actions = torch.argmax(self.q_table[torch.arange(self.num_envs).view(-1, 1), inputs["states"]],
dim=-1, keepdim=True).view(-1,1)
# choose random actions for exploration according to epsilon
indexes = (torch.rand(inputs["states"].shape[0], device=self.device) < self.epsilon).nonzero().view(-1)
if indexes.numel():
actions[indexes] = torch.randint(self.num_actions, (indexes.numel(), 1), device=self.device)
return actions, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("FrozenLake-v0")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("FrozenLake-v")][0]
print("FrozenLake-v0 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate the agent's model (table)
# Q-learning requires 1 model, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/q_learning.html#models
models = {}
models["policy"] = EpilonGreedyPolicy(env.observation_space, env.action_space, device, num_envs=env.num_envs, epsilon=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/q_learning.html#configuration-and-hyperparameters
cfg = Q_LEARNING_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.999
cfg["alpha"] = 0.4
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1600
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/FrozenLake"
agent = Q_LEARNING(models=models,
memory=None,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 80000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,168 | Python | 37.646341 | 122 | 0.691604 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_trpo.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.trpo import TRPO, TRPO_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(inputs["states"])), self.log_std_parameter, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, 1))
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.vector.make("PendulumNoVel-v1", num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# TRPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True)
models["value"] = Value(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#configuration-and-hyperparameters
cfg = TRPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["grad_norm_clip"] = 0.5
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = TRPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,533 | Python | 38.771929 | 117 | 0.678359 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_sac_rnn.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.sac import SAC_DEFAULT_CONFIG
from skrl.agents.torch.sac import SAC_RNN as SAC
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Actor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), self.log_std_parameter, {"rnn": [hidden_states]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role in ["target_critic_1", "target_critic_2"] else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg = SAC_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
cfg["learn_entropy"] = True
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = SAC(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 9,935 | Python | 44.577981 | 146 | 0.637947 |
Toni-SM/skrl/docs/source/examples/gymnasium/jax_gymnasium_cartpole_dqn.py | import gymnasium as gym
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.dqn import DQN, DQN_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
from skrl.utils.model_instantiators.jax import Shape, deterministic_model
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("CartPole-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("CartPole-v")][0]
print("CartPole-v0 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=50000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators) using the model instantiator utility.
# DQN requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#models
models = {}
models["q_network"] = deterministic_model(observation_space=env.observation_space,
action_space=env.action_space,
device=device,
clip_actions=False,
input_shape=Shape.OBSERVATIONS,
hiddens=[64, 64],
hidden_activation=["relu", "relu"],
output_shape=Shape.ACTIONS,
output_activation=None,
output_scale=1.0)
models["target_q_network"] = deterministic_model(observation_space=env.observation_space,
action_space=env.action_space,
device=device,
clip_actions=False,
input_shape=Shape.OBSERVATIONS,
hiddens=[64, 64],
hidden_activation=["relu", "relu"],
output_shape=Shape.ACTIONS,
output_activation=None,
output_scale=1.0)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#configuration-and-hyperparameters
cfg = DQN_DEFAULT_CONFIG.copy()
cfg["learning_starts"] = 100
cfg["exploration"]["final_epsilon"] = 0.04
cfg["exploration"]["timesteps"] = 1500
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/jax/CartPole"
agent = DQN(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 50000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,963 | Python | 40.291666 | 97 | 0.59248 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_ppo.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(inputs["states"])), self.log_std_parameter, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, 1))
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.vector.make("PendulumNoVel-v1", num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True)
models["value"] = Value(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["grad_norm_clip"] = 0.5
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = False
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 0.5
cfg["kl_threshold"] = 0
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,857 | Python | 38.495935 | 117 | 0.680873 |
Toni-SM/skrl/docs/source/examples/gymnasium/jax_gymnasium_pendulum_td3.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, Model
from skrl.resources.noises.jax import GaussianNoise
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(400)(inputs["states"]))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(self.num_actions)(x)
# Pendulum-v1 action_space is -2 to 2
return 2 * nn.tanh(x), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(400)(x))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("Pendulum-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 1000
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/jax/Pendulum"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,438 | Python | 36.302521 | 99 | 0.71023 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_ddpg.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 1000
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,381 | Python | 37.778761 | 117 | 0.71696 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_frozen_lake_vector_q_learning.py | import gymnasium as gym
import torch
# import the skrl components to build the RL system
from skrl.agents.torch.q_learning import Q_LEARNING, Q_LEARNING_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.models.torch import Model, TabularMixin
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define model (tabular model) using mixin
class EpilonGreedyPolicy(TabularMixin, Model):
def __init__(self, observation_space, action_space, device, num_envs=1, epsilon=0.1):
Model.__init__(self, observation_space, action_space, device)
TabularMixin.__init__(self, num_envs)
self.epsilon = epsilon
self.q_table = torch.ones((num_envs, self.num_observations, self.num_actions),
dtype=torch.float32, device=self.device)
def compute(self, inputs, role):
actions = torch.argmax(self.q_table[torch.arange(self.num_envs).view(-1, 1), inputs["states"]],
dim=-1, keepdim=True).view(-1,1)
# choose random actions for exploration according to epsilon
indexes = (torch.rand(inputs["states"].shape[0], device=self.device) < self.epsilon).nonzero().view(-1)
if indexes.numel():
actions[indexes] = torch.randint(self.num_actions, (indexes.numel(), 1), device=self.device)
return actions, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("FrozenLake-v0", num_envs=10, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("FrozenLake-v")][0]
print("FrozenLake-v0 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=10, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate the agent's model (table)
# Q-learning requires 1 model, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/q_learning.html#models
models = {}
models["policy"] = EpilonGreedyPolicy(env.observation_space, env.action_space, device, num_envs=env.num_envs, epsilon=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/q_learning.html#configuration-and-hyperparameters
cfg = Q_LEARNING_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.999
cfg["alpha"] = 0.4
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1600
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/FrozenLake"
agent = Q_LEARNING(models=models,
memory=None,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 80000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,248 | Python | 38.621951 | 122 | 0.694273 |
Toni-SM/skrl/docs/source/examples/gymnasium/jax_gymnasium_cartpole_vector_dqn.py | import gymnasium as gym
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.dqn import DQN, DQN_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
from skrl.utils.model_instantiators.jax import Shape, deterministic_model
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("CartPole-v1", num_envs=5, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("CartPole-v")][0]
print("CartPole-v0 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=5, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=200000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators) using the model instantiator utility.
# DQN requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#models
models = {}
models["q_network"] = deterministic_model(observation_space=env.observation_space,
action_space=env.action_space,
device=device,
clip_actions=False,
input_shape=Shape.OBSERVATIONS,
hiddens=[64, 64],
hidden_activation=["relu", "relu"],
output_shape=Shape.ACTIONS,
output_activation=None,
output_scale=1.0)
models["target_q_network"] = deterministic_model(observation_space=env.observation_space,
action_space=env.action_space,
device=device,
clip_actions=False,
input_shape=Shape.OBSERVATIONS,
hiddens=[64, 64],
hidden_activation=["relu", "relu"],
output_shape=Shape.ACTIONS,
output_activation=None,
output_scale=1.0)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#configuration-and-hyperparameters
cfg = DQN_DEFAULT_CONFIG.copy()
cfg["learning_starts"] = 100
cfg["exploration"]["final_epsilon"] = 0.04
cfg["exploration"]["timesteps"] = 1500
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/jax/CartPole"
agent = DQN(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 50000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,042 | Python | 41.114583 | 98 | 0.596487 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_td3.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import GaussianNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 1000
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,598 | Python | 38.307692 | 117 | 0.717921 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulum_ppo.py | import gymnasium as gym
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(inputs["states"])), self.log_std_parameter, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, 1))
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("Pendulum-v1", num_envs=4, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True)
models["value"] = Value(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["grad_norm_clip"] = 0.5
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = False
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 0.5
cfg["kl_threshold"] = 0
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/Pendulum"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,791 | Python | 38.933333 | 101 | 0.67752 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_trpo_lstm.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.trpo import TRPO_DEFAULT_CONFIG
from skrl.agents.torch.trpo import TRPO_RNN as TRPO
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum",
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,0,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.net(rnn_output)), self.log_std_parameter, {"rnn": [rnn_states[0], rnn_states[1]]}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=64, sequence_length=128):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hcell (Hout is Hcell because proj_size = 0)
self.sequence_length = sequence_length
self.lstm = nn.LSTM(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.net = nn.Sequential(nn.Linear(self.hidden_size, 64),
nn.ReLU(),
nn.Linear(64, 1))
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size), # hidden states (D ∗ num_layers, N, Hout)
(self.num_layers, self.num_envs, self.hidden_size)]}} # cell states (D ∗ num_layers, N, Hcell)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states, cell_states = inputs["rnn"][0], inputs["rnn"][1]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
cell_states = cell_states.view(self.num_layers, -1, self.sequence_length, cell_states.shape[-1]) # (D * num_layers, N, L, Hcell)
# get the hidden/cell states corresponding to the initial sequence
hidden_states = hidden_states[:,:,0,:].contiguous() # (D * num_layers, N, Hout)
cell_states = cell_states[:,:,0,:].contiguous() # (D * num_layers, N, Hcell)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, (hidden_states, cell_states) = self.lstm(rnn_input[:,i0:i1,:], (hidden_states, cell_states))
hidden_states[:, (terminated[:,i1-1]), :] = 0
cell_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_states = (hidden_states, cell_states)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, rnn_states = self.lstm(rnn_input, (hidden_states, cell_states))
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
return self.net(rnn_output), {"rnn": [rnn_states[0], rnn_states[1]]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.vector.make("PendulumNoVel-v1", num_envs=4, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1024, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# TRPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True, num_envs=env.num_envs)
models["value"] = Value(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/trpo.html#configuration-and-hyperparameters
cfg = TRPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1024 # memory_size
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 32
cfg["discount_factor"] = 0.9
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["grad_norm_clip"] = 0.5
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 500
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = TRPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 11,017 | Python | 48.1875 | 146 | 0.613234 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_td3_rnn.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.td3 import TD3_DEFAULT_CONFIG
from skrl.agents.torch.td3 import TD3_RNN as TD3
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import GaussianNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role == "target_policy" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {"rnn": [hidden_states]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.rnn = nn.RNN(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role in ["target_critic_1", "target_critic_2"] else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.rnn(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.rnn(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 10,106 | Python | 44.940909 | 146 | 0.641599 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulum_ddpg.py | import gymnasium as gym
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("Pendulum-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=15000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["batch_size"] = 100
cfg["random_timesteps"] = 100
cfg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/Pendulum"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,244 | Python | 37.944954 | 106 | 0.713242 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulum_sac.py | import gymnasium as gym
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Actor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), self.log_std_parameter, {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("Pendulum-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, clip_actions=True)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg = SAC_DEFAULT_CONFIG.copy()
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
cfg["learn_entropy"] = True
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/Pendulum"
agent = SAC(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,480 | Python | 38.654867 | 102 | 0.708259 |
Toni-SM/skrl/docs/source/examples/gymnasium/jax_gymnasium_pendulum_vector_ddpg.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, Model
from skrl.resources.noises.jax import OrnsteinUhlenbeckNoise
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixins
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(400)(inputs["states"]))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(self.num_actions)(x)
# Pendulum-v1 action_space is -2 to 2
return 2 * nn.tanh(x), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(400)(x))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.vector.make("Pendulum-v1", num_envs=10, asynchronous=False)
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.vector.make(env_id, num_envs=10, asynchronous=False)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=100000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["batch_size"] = 100
cfg["random_timesteps"] = 100
cfg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/jax/Pendulum"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,271 | Python | 36.473684 | 106 | 0.710841 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulumnovel_ddpg_gru.py | import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG_DEFAULT_CONFIG
from skrl.agents.torch.ddpg import DDPG_RNN as DDPG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# training
if self.training:
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role == "target_policy" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# rollout
else:
rnn_input = states.view(-1, 1, states.shape[-1]) # (N, L, Hin): N=num_envs, L=1
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(rnn_output))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {"rnn": [hidden_states]}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
num_envs=1, num_layers=1, hidden_size=400, sequence_length=20):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.num_envs = num_envs
self.num_layers = num_layers
self.hidden_size = hidden_size # Hout
self.sequence_length = sequence_length
self.gru = nn.GRU(input_size=self.num_observations,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True) # batch_first -> (batch, sequence, features)
self.linear_layer_1 = nn.Linear(self.hidden_size + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def get_specification(self):
# batch size (N) is the number of envs
return {"rnn": {"sequence_length": self.sequence_length,
"sizes": [(self.num_layers, self.num_envs, self.hidden_size)]}} # hidden states (D ∗ num_layers, N, Hout)
def compute(self, inputs, role):
states = inputs["states"]
terminated = inputs.get("terminated", None)
hidden_states = inputs["rnn"][0]
# critic is only used during training
rnn_input = states.view(-1, self.sequence_length, states.shape[-1]) # (N, L, Hin): N=batch_size, L=sequence_length
hidden_states = hidden_states.view(self.num_layers, -1, self.sequence_length, hidden_states.shape[-1]) # (D * num_layers, N, L, Hout)
# get the hidden states corresponding to the initial sequence
sequence_index = 1 if role == "target_critic" else 0 # target networks act on the next state of the environment
hidden_states = hidden_states[:,:,sequence_index,:].contiguous() # (D * num_layers, N, Hout)
# reset the RNN state in the middle of a sequence
if terminated is not None and torch.any(terminated):
rnn_outputs = []
terminated = terminated.view(-1, self.sequence_length)
indexes = [0] + (terminated[:,:-1].any(dim=0).nonzero(as_tuple=True)[0] + 1).tolist() + [self.sequence_length]
for i in range(len(indexes) - 1):
i0, i1 = indexes[i], indexes[i + 1]
rnn_output, hidden_states = self.gru(rnn_input[:,i0:i1,:], hidden_states)
hidden_states[:, (terminated[:,i1-1]), :] = 0
rnn_outputs.append(rnn_output)
rnn_output = torch.cat(rnn_outputs, dim=1)
# no need to reset the RNN state in the sequence
else:
rnn_output, hidden_states = self.gru(rnn_input, hidden_states)
# flatten the RNN output
rnn_output = torch.flatten(rnn_output, start_dim=0, end_dim=1) # (N, L, D ∗ Hout) -> (N * L, D ∗ Hout)
x = F.relu(self.linear_layer_1(torch.cat([rnn_output, inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {"rnn": [hidden_states]}
# environment observation wrapper used to mask velocity. Adapted from rl_zoo3 (rl_zoo3/wrappers.py)
class NoVelocityWrapper(gym.ObservationWrapper):
def observation(self, observation):
# observation: x, y, angular velocity
return observation * np.array([1, 1, 0])
gym.envs.registration.register(id="PendulumNoVel-v1", entry_point=lambda: NoVelocityWrapper(gym.make("Pendulum-v1")))
# load and wrap the gymnasium environment
env = gym.make("PendulumNoVel-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_policy"] = Actor(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["critic"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
models["target_critic"] = Critic(env.observation_space, env.action_space, device, num_envs=env.num_envs)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/PendulumNoVel"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 9,822 | Python | 44.476852 | 146 | 0.638974 |
Toni-SM/skrl/docs/source/examples/gymnasium/torch_gymnasium_pendulum_td3.py | import gymnasium as gym
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import GaussianNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("Pendulum-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 1000
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/Pendulum"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,493 | Python | 38.421052 | 102 | 0.714222 |
Toni-SM/skrl/docs/source/examples/gymnasium/jax_gymnasium_pendulum_ddpg.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, Model
from skrl.resources.noises.jax import OrnsteinUhlenbeckNoise
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixins
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(400)(inputs["states"]))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(self.num_actions)(x)
# Pendulum-v1 action_space is -2 to 2
return 2 * nn.tanh(x), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(400)(x))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the gymnasium environment.
# note: the environment version may change depending on the gymnasium version
try:
env = gym.make("Pendulum-v1")
except (gym.error.DeprecatedEnv, gym.error.VersionNotFound) as e:
env_id = [spec for spec in gym.envs.registry if spec.startswith("Pendulum-v")][0]
print("Pendulum-v1 not found. Trying {}".format(env_id))
env = gym.make(env_id)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=15000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["batch_size"] = 100
cfg["random_timesteps"] = 100
cfg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/jax/Pendulum"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 4,190 | Python | 35.763158 | 106 | 0.709069 |
Toni-SM/skrl/docs/source/examples/isaacsim/torch_isaacsim_cartpole_ppo.py | # Omniverse Isaac Sim tutorial: Creating New RL Environment
# https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_gym_new_rl_example.html
# instantiate the VecEnvBase and create the task
from omni.isaac.gym.vec_env import VecEnvBase # isort: skip
env = VecEnvBase(headless=True)
from cartpole_task import CartpoleTask # isort: skip
task = CartpoleTask(name="Cartpole")
env.set_task(task, backend="torch")
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.Tanh(),
nn.Linear(64, 64),
nn.Tanh(),
nn.Linear(64, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
return torch.tanh(self.net(inputs["states"])), self.log_std_parameter, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.Tanh(),
nn.Linear(64, 64),
nn.Tanh(),
nn.Linear(64, 1))
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
# load and wrap the environment
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=1000, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True)
models["value"] = Value(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 1000 # memory_size
cfg["learning_epochs"] = 20
cfg["mini_batches"] = 1
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = False
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 0.5
cfg["kl_threshold"] = 0
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 10000
cfg["experiment"]["directory"] = "runs/torch/Cartpole"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 100000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 4,354 | Python | 35.906779 | 101 | 0.673404 |
Toni-SM/skrl/docs/source/examples/isaacsim/torch_isaacsim_jetbot_ppo.py | # import JetBot environment
from env import JetBotEnv
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.net = nn.Sequential(nn.Conv2d(3, 32, kernel_size=8, stride=4),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1),
nn.ReLU(),
nn.Flatten(),
nn.Linear(9216, 512),
nn.ReLU(),
nn.Linear(512, 16),
nn.Tanh(),
nn.Linear(16, 64),
nn.Tanh(),
nn.Linear(64, 32),
nn.Tanh(),
nn.Linear(32, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
# view (samples, width * height * channels) -> (samples, width, height, channels)
# permute (samples, width, height, channels) -> (samples, channels, width, height)
x = self.net(inputs["states"].view(-1, *self.observation_space.shape).permute(0, 3, 1, 2))
return 10 * torch.tanh(x), self.log_std_parameter, {} # JetBotEnv action_space is -10 to 10
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Conv2d(3, 32, kernel_size=8, stride=4),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1),
nn.ReLU(),
nn.Flatten(),
nn.Linear(9216, 512),
nn.ReLU(),
nn.Linear(512, 16),
nn.Tanh(),
nn.Linear(16, 64),
nn.Tanh(),
nn.Linear(64, 32),
nn.Tanh(),
nn.Linear(32, 1))
def compute(self, inputs, role):
# view (samples, width * height * channels) -> (samples, width, height, channels)
# permute (samples, width, height, channels) -> (samples, channels, width, height)
return self.net(inputs["states"].view(-1, *self.observation_space.shape).permute(0, 3, 1, 2)), {}
# load and wrap the environment
env = JetBotEnv(headless=True)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=10000, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device, clip_actions=True)
models["value"] = Value(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 10000
cfg["learning_epochs"] = 10
cfg["mini_batches"] = 10
cfg["discount_factor"] = 0.9995
cfg["lambda"] = 0.95
cfg["policy_learning_rate"] = 2.5e-4
cfg["value_learning_rate"] = 2.5e-4
cfg["grad_norm_clip"] = 10
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = False
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 0.5
cfg["kl_threshold"] = 0
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 10000
cfg["experiment"]["checkpoint_interval"] = 10000
cfg["experiment"]["directory"] = "runs/torch/JetBotEnv"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 500000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,771 | Python | 40.826087 | 105 | 0.576157 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ingenuity_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU())
self.mean_layer = nn.Linear(128, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(128, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ingenuity")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 4 # 16 * 4096 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 32
cfg["experiment"]["checkpoint_interval"] = 320
cfg["experiment"]["directory"] = "runs/torch/Ingenuity"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 6400, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-Ingenuity-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,044 | Python | 37.807692 | 101 | 0.670301 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_factory_task_nut_bolt_pick_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU())
self.mean_layer = nn.Linear(64, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(64, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="FactoryTaskNutBoltPick")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=240, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 240 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 60 # 240 * 128 / 512
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-4
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0.016
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 614
cfg["experiment"]["checkpoint_interval"] = 6144
cfg["experiment"]["directory"] = "runs/torch/FactoryTaskNutBoltPick"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 120000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 4,311 | Python | 37.159292 | 101 | 0.678265 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ant_ddpg_td3_sac_parallel_unshared_memory.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.agents.torch.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.agents.torch.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.noises.torch import GaussianNoise, OrnsteinUhlenbeckNoise
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import ParallelTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class StochasticActor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-5, max_log_std=2):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
return self.net(inputs["states"]), self.log_std_parameter, {}
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
if __name__ == '__main__':
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=192)
env = wrap_env(env)
device = env.device
# instantiate memories as experience replay (unique for each agents).
# scopes (192 envs): DDPG 64, TD3 64 and SAC 64
memory_ddpg = RandomMemory(memory_size=15625, num_envs=64, device=device)
memory_td3 = RandomMemory(memory_size=15625, num_envs=64, device=device)
memory_sac = RandomMemory(memory_size=15625, num_envs=64, device=device)
# instantiate the agents' models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models_ddpg = {}
models_ddpg["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_ddpg["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_ddpg["critic"] = Critic(env.observation_space, env.action_space, device)
models_ddpg["target_critic"] = Critic(env.observation_space, env.action_space, device)
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models_td3 = {}
models_td3["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_td3["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_td3["critic_1"] = Critic(env.observation_space, env.action_space, device)
models_td3["critic_2"] = Critic(env.observation_space, env.action_space, device)
models_td3["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models_td3["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models_sac = {}
models_sac["policy"] = StochasticActor(env.observation_space, env.action_space, device, clip_actions=True)
models_sac["critic_1"] = Critic(env.observation_space, env.action_space, device)
models_sac["critic_2"] = Critic(env.observation_space, env.action_space, device)
models_sac["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models_sac["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# configure and instantiate the agents (visit their documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg_ddpg = DDPG_DEFAULT_CONFIG.copy()
cfg_ddpg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=0.5, device=device)
cfg_ddpg["gradient_steps"] = 1
cfg_ddpg["batch_size"] = 4096
cfg_ddpg["discount_factor"] = 0.99
cfg_ddpg["polyak"] = 0.005
cfg_ddpg["actor_learning_rate"] = 5e-4
cfg_ddpg["critic_learning_rate"] = 5e-4
cfg_ddpg["random_timesteps"] = 80
cfg_ddpg["learning_starts"] = 80
cfg_ddpg["state_preprocessor"] = RunningStandardScaler
cfg_ddpg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_ddpg["experiment"]["write_interval"] = 800
cfg_ddpg["experiment"]["checkpoint_interval"] = 8000
cfg_ddpg["experiment"]["directory"] = "runs/torch/Ant"
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg_td3 = TD3_DEFAULT_CONFIG.copy()
cfg_td3["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg_td3["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg_td3["smooth_regularization_clip"] = 0.5
cfg_td3["gradient_steps"] = 1
cfg_td3["batch_size"] = 4096
cfg_td3["discount_factor"] = 0.99
cfg_td3["polyak"] = 0.005
cfg_td3["actor_learning_rate"] = 5e-4
cfg_td3["critic_learning_rate"] = 5e-4
cfg_td3["random_timesteps"] = 80
cfg_td3["learning_starts"] = 80
cfg_td3["state_preprocessor"] = RunningStandardScaler
cfg_td3["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_td3["experiment"]["write_interval"] = 800
cfg_td3["experiment"]["checkpoint_interval"] = 8000
cfg_td3["experiment"]["directory"] = "runs/torch/Ant"
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg_sac = SAC_DEFAULT_CONFIG.copy()
cfg_sac["gradient_steps"] = 1
cfg_sac["batch_size"] = 4096
cfg_sac["discount_factor"] = 0.99
cfg_sac["polyak"] = 0.005
cfg_sac["actor_learning_rate"] = 5e-4
cfg_sac["critic_learning_rate"] = 5e-4
cfg_sac["random_timesteps"] = 80
cfg_sac["learning_starts"] = 80
cfg_sac["grad_norm_clip"] = 0
cfg_sac["learn_entropy"] = True
cfg_sac["entropy_learning_rate"] = 5e-3
cfg_sac["initial_entropy_value"] = 1.0
cfg_sac["state_preprocessor"] = RunningStandardScaler
cfg_sac["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_sac["experiment"]["write_interval"] = 800
cfg_sac["experiment"]["checkpoint_interval"] = 8000
cfg_sac["experiment"]["directory"] = "runs/torch/Ant"
agent_ddpg = DDPG(models=models_ddpg,
memory=memory_ddpg,
cfg=cfg_ddpg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
agent_td3 = TD3(models=models_td3,
memory=memory_td3,
cfg=cfg_td3,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
agent_sac = SAC(models=models_sac,
memory=memory_sac,
cfg=cfg_sac,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer and define the agent scopes
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = ParallelTrainer(cfg=cfg_trainer,
env=env,
agents=[agent_ddpg, agent_td3, agent_sac],
agents_scope=[64, 64, 64]) # scopes (192 envs): DDPG 64, TD3 64 and SAC 64
# start training
trainer.train()
| 9,846 | Python | 46.800971 | 115 | 0.643612 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ant_td3.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import GaussianNoise
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixins
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=64)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=15625, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# TD3 requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["gradient_steps"] = 1
cfg["batch_size"] = 4096
cfg["discount_factor"] = 0.99
cfg["polyak"] = 0.005
cfg["actor_learning_rate"] = 5e-4
cfg["critic_learning_rate"] = 5e-4
cfg["random_timesteps"] = 80
cfg["learning_starts"] = 80
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/Ant"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 4,422 | Python | 39.577981 | 93 | 0.68227 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_ant_mt_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import threading
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the multi-threaded Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", multi_threaded=True, timeout=30)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 4
cfg["mini_batches"] = 2 # 16 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 40
cfg["experiment"]["checkpoint_interval"] = 400
cfg["experiment"]["directory"] = "runs/jax/Ant"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 8000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training in a separate thread
threading.Thread(target=trainer.train).start()
# run the simulation in the main thread
env.run()
| 5,683 | Python | 37.405405 | 102 | 0.70315 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_cartpole_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(40)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(32)(inputs["states"]))
x = nn.elu(nn.Dense(32)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(32)(inputs["states"]))
x = nn.elu(nn.Dense(32)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Cartpole")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 1 # 16 * 512 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 16
cfg["experiment"]["checkpoint_interval"] = 80
cfg["experiment"]["directory"] = "runs/jax/Cartpole"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 1600, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,443 | Python | 37.885714 | 102 | 0.704024 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_shadow_hand_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(512)(inputs["states"]))
x = nn.elu(nn.Dense(512)(x))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(512)(inputs["states"]))
x = nn.elu(nn.Dense(512)(x))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="ShadowHand")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 4 # 16 * 8192 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 5e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/jax/ShadowHand"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,607 | Python | 37.944444 | 102 | 0.698413 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_allegro_hand_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ELU(),
nn.Linear(512, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU())
self.mean_layer = nn.Linear(128, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(128, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="AllegroHand")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 4 # 16 * 8192 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 5e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.02}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/AllegroHand"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-AllegroHand-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,053 | Python | 37.876923 | 102 | 0.670889 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_humanoid_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 400),
nn.ELU(),
nn.Linear(400, 200),
nn.ELU(),
nn.Linear(200, 100),
nn.ELU())
self.mean_layer = nn.Linear(100, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(100, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Humanoid")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=32, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 32 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 4 # 32 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 5e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 160
cfg["experiment"]["checkpoint_interval"] = 1600
cfg["experiment"]["directory"] = "runs/torch/Humanoid"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 32000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-Humanoid-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,044 | Python | 37.807692 | 101 | 0.670301 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_ant_sac.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class StochasticActor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-5, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(512)(inputs["states"]))
x = nn.relu(nn.Dense(256)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return nn.tanh(x), log_std, {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(512)(x))
x = nn.relu(nn.Dense(256)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=64)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=15625, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models = {}
models["policy"] = StochasticActor(env.observation_space, env.action_space, device)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg = SAC_DEFAULT_CONFIG.copy()
cfg["gradient_steps"] = 1
cfg["batch_size"] = 4096
cfg["discount_factor"] = 0.99
cfg["polyak"] = 0.005
cfg["actor_learning_rate"] = 5e-4
cfg["critic_learning_rate"] = 5e-4
cfg["random_timesteps"] = 80
cfg["learning_starts"] = 80
cfg["grad_norm_clip"] = 0
cfg["learn_entropy"] = True
cfg["entropy_learning_rate"] = 5e-3
cfg["initial_entropy_value"] = 1.0
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/jax/Ant"
agent = SAC(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,366 | Python | 38.755555 | 102 | 0.706113 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_ingenuity_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
seed = set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ingenuity")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 4 # 16 * 4096 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 32
cfg["experiment"]["checkpoint_interval"] = 320
cfg["experiment"]["directory"] = "runs/jax/Ingenuity"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 6400, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,534 | Python | 37.978873 | 102 | 0.70112 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ant_sac.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class StochasticActor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-5, max_log_std=2):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
return self.net(inputs["states"]), self.log_std_parameter, {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=64)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=15625, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models = {}
models["policy"] = StochasticActor(env.observation_space, env.action_space, device)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg = SAC_DEFAULT_CONFIG.copy()
cfg["gradient_steps"] = 1
cfg["batch_size"] = 4096
cfg["discount_factor"] = 0.99
cfg["polyak"] = 0.005
cfg["actor_learning_rate"] = 5e-4
cfg["critic_learning_rate"] = 5e-4
cfg["random_timesteps"] = 80
cfg["learning_starts"] = 80
cfg["grad_norm_clip"] = 0
cfg["learn_entropy"] = True
cfg["entropy_learning_rate"] = 5e-3
cfg["initial_entropy_value"] = 1.0
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/Ant"
agent = SAC(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 4,440 | Python | 39.372727 | 93 | 0.673874 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ant_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU())
self.mean_layer = nn.Linear(64, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(64, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 4
cfg["mini_batches"] = 2 # 16 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 40
cfg["experiment"]["checkpoint_interval"] = 400
cfg["experiment"]["directory"] = "runs/torch/Ant"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 8000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-Ant-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,023 | Python | 37.646154 | 101 | 0.668923 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ant_ddpg_td3_sac_sequential_shared_memory.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.agents.torch.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.agents.torch.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.noises.torch import GaussianNoise, OrnsteinUhlenbeckNoise
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class StochasticActor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-5, max_log_std=2):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
return self.net(inputs["states"]), self.log_std_parameter, {}
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=64)
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay (unique to all agents)
memory = RandomMemory(memory_size=15625, num_envs=env.num_envs, device=device)
# instantiate the agents' models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models_ddpg = {}
models_ddpg["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_ddpg["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_ddpg["critic"] = Critic(env.observation_space, env.action_space, device)
models_ddpg["target_critic"] = Critic(env.observation_space, env.action_space, device)
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models_td3 = {}
models_td3["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_td3["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_td3["critic_1"] = Critic(env.observation_space, env.action_space, device)
models_td3["critic_2"] = Critic(env.observation_space, env.action_space, device)
models_td3["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models_td3["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models_sac = {}
models_sac["policy"] = StochasticActor(env.observation_space, env.action_space, device, clip_actions=True)
models_sac["critic_1"] = Critic(env.observation_space, env.action_space, device)
models_sac["critic_2"] = Critic(env.observation_space, env.action_space, device)
models_sac["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models_sac["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# configure and instantiate the agents (visit their documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg_ddpg = DDPG_DEFAULT_CONFIG.copy()
cfg_ddpg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=0.5, device=device)
cfg_ddpg["gradient_steps"] = 1
cfg_ddpg["batch_size"] = 4096
cfg_ddpg["discount_factor"] = 0.99
cfg_ddpg["polyak"] = 0.005
cfg_ddpg["actor_learning_rate"] = 5e-4
cfg_ddpg["critic_learning_rate"] = 5e-4
cfg_ddpg["random_timesteps"] = 80
cfg_ddpg["learning_starts"] = 80
cfg_ddpg["state_preprocessor"] = RunningStandardScaler
cfg_ddpg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_ddpg["experiment"]["write_interval"] = 800
cfg_ddpg["experiment"]["checkpoint_interval"] = 8000
cfg_ddpg["experiment"]["directory"] = "runs/torch/Ant"
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg_td3 = TD3_DEFAULT_CONFIG.copy()
cfg_td3["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg_td3["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg_td3["smooth_regularization_clip"] = 0.5
cfg_td3["gradient_steps"] = 1
cfg_td3["batch_size"] = 4096
cfg_td3["discount_factor"] = 0.99
cfg_td3["polyak"] = 0.005
cfg_td3["actor_learning_rate"] = 5e-4
cfg_td3["critic_learning_rate"] = 5e-4
cfg_td3["random_timesteps"] = 80
cfg_td3["learning_starts"] = 80
cfg_td3["state_preprocessor"] = RunningStandardScaler
cfg_td3["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_td3["experiment"]["write_interval"] = 800
cfg_td3["experiment"]["checkpoint_interval"] = 8000
cfg_td3["experiment"]["directory"] = "runs/torch/Ant"
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg_sac = SAC_DEFAULT_CONFIG.copy()
cfg_sac["gradient_steps"] = 1
cfg_sac["batch_size"] = 4096
cfg_sac["discount_factor"] = 0.99
cfg_sac["polyak"] = 0.005
cfg_sac["actor_learning_rate"] = 5e-4
cfg_sac["critic_learning_rate"] = 5e-4
cfg_sac["random_timesteps"] = 80
cfg_sac["learning_starts"] = 80
cfg_sac["grad_norm_clip"] = 0
cfg_sac["learn_entropy"] = True
cfg_sac["entropy_learning_rate"] = 5e-3
cfg_sac["initial_entropy_value"] = 1.0
cfg_sac["state_preprocessor"] = RunningStandardScaler
cfg_sac["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_sac["experiment"]["write_interval"] = 800
cfg_sac["experiment"]["checkpoint_interval"] = 8000
cfg_sac["experiment"]["directory"] = "runs/torch/Ant"
agent_ddpg = DDPG(models=models_ddpg,
memory=memory, # shared memory
cfg=cfg_ddpg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
agent_td3 = TD3(models=models_td3,
memory=memory, # shared memory
cfg=cfg_td3,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
agent_sac = SAC(models=models_sac,
memory=memory, # shared memory
cfg=cfg_sac,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer,
env=env,
agents=[agent_ddpg, agent_td3, agent_sac],
agents_scope=[])
# start training
trainer.train()
| 9,118 | Python | 44.368159 | 111 | 0.67449 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_anymal_terrain_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(512)(inputs["states"]))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(512)(inputs["states"]))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="AnymalTerrain")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=48, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 48 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 6 # 48 * 2048 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.001
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 480
cfg["experiment"]["checkpoint_interval"] = 4800
cfg["experiment"]["directory"] = "runs/jax/AnymalTerrain"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 96000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,493 | Python | 37.690141 | 102 | 0.701438 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_crazyflie_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.Tanh(),
nn.Linear(256, 256),
nn.Tanh(),
nn.Linear(256, 128),
nn.Tanh())
self.mean_layer = nn.Linear(128, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(128, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Crazyflie")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 4 # 16 * 4096 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 80
cfg["experiment"]["checkpoint_interval"] = 800
cfg["experiment"]["directory"] = "runs/torch/Crazyflie"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 16000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-Crazyflie-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,048 | Python | 37.838461 | 101 | 0.670563 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_ball_balance_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(128)(inputs["states"]))
x = nn.elu(nn.Dense(64)(x))
x = nn.elu(nn.Dense(32)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(128)(inputs["states"]))
x = nn.elu(nn.Dense(64)(x))
x = nn.elu(nn.Dense(32)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="BallBalance")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 8 # 16 * 4096 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 20
cfg["experiment"]["checkpoint_interval"] = 200
cfg["experiment"]["directory"] = "runs/jax/BallBalance"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 4000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,525 | Python | 37.915493 | 102 | 0.701176 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_ant_ddpg.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, Model
from skrl.resources.noises.jax import OrnsteinUhlenbeckNoise
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixins
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(512)(inputs["states"]))
x = nn.relu(nn.Dense(256)(x))
x = nn.Dense(self.num_actions)(x)
return nn.tanh(x), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(512)(x))
x = nn.relu(nn.Dense(256)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=64)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=15625, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=0.5, device=device)
cfg["gradient_steps"] = 1
cfg["batch_size"] = 4096
cfg["discount_factor"] = 0.99
cfg["polyak"] = 0.005
cfg["actor_learning_rate"] = 5e-4
cfg["critic_learning_rate"] = 5e-4
cfg["random_timesteps"] = 80
cfg["learning_starts"] = 80
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/jax/Ant"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,114 | Python | 38.346154 | 106 | 0.713531 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_allegro_hand_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
seed = set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(512)(inputs["states"]))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(512)(inputs["states"]))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="AllegroHand")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 4 # 16 * 8192 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 5e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.02}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/jax/AllegroHand"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,541 | Python | 38.028169 | 102 | 0.701498 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_anymal_terrain_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ELU(),
nn.Linear(512, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, self.num_actions))
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
return self.net(inputs["states"]), self.log_std_parameter, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ELU(),
nn.Linear(512, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 1))
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="AnymalTerrain")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=48, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 48 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 6 # 48 * 2048 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.001
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 480
cfg["experiment"]["checkpoint_interval"] = 4800
cfg["experiment"]["directory"] = "runs/torch/AnymalTerrain"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 96000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,238 | Python | 38.390977 | 104 | 0.649485 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_cartpole_mt_ppo.py | import threading
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(40)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 32),
nn.ELU(),
nn.Linear(32, 32),
nn.ELU())
self.mean_layer = nn.Linear(32, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(32, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the multi-threaded Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Cartpole", multi_threaded=True, timeout=30)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 1 # 16 * 512 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 16
cfg["experiment"]["checkpoint_interval"] = 80
cfg["experiment"]["directory"] = "runs/torch/Cartpole"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 1600, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training in a separate thread
threading.Thread(target=trainer.train).start()
# run the simulation in the main thread
env.run()
| 4,557 | Python | 36.983333 | 101 | 0.694536 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_humanoid_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(400)(inputs["states"]))
x = nn.elu(nn.Dense(200)(x))
x = nn.elu(nn.Dense(100)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(400)(inputs["states"]))
x = nn.elu(nn.Dense(200)(x))
x = nn.elu(nn.Dense(100)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Humanoid")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=32, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 32 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 4 # 32 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 5e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 160
cfg["experiment"]["checkpoint_interval"] = 1600
cfg["experiment"]["directory"] = "runs/jax/Humanoid"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 32000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,528 | Python | 37.936619 | 102 | 0.701339 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_shadow_hand_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ELU(),
nn.Linear(512, 512),
nn.ELU(),
nn.Linear(512, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU())
self.mean_layer = nn.Linear(128, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(128, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="ShadowHand")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 4 # 16 * 8192 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 5e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/ShadowHand"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-ShadowHand-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,148 | Python | 38.007575 | 101 | 0.66181 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_franka_cabinet_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU())
self.mean_layer = nn.Linear(64, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(64, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="FrankaCabinet")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 8 # 16 * 4096 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 5e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 120
cfg["experiment"]["checkpoint_interval"] = 1200
cfg["experiment"]["directory"] = "runs/torch/FrankaCabinet"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 24000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-FrankaCabinet-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,055 | Python | 37.892307 | 104 | 0.671019 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_quadcopter_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(256)(x))
x = nn.elu(nn.Dense(128)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Quadcopter")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 4 # 16 * 4096 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 80
cfg["experiment"]["checkpoint_interval"] = 800
cfg["experiment"]["directory"] = "runs/jax/Quadcopter"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 16000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,529 | Python | 37.943662 | 102 | 0.701393 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_anymal_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Anymal")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=24, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 24 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 3 # 24 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 120
cfg["experiment"]["checkpoint_interval"] = 1200
cfg["experiment"]["directory"] = "runs/jax/Anymal"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 24000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,475 | Python | 37.56338 | 102 | 0.700457 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_cartpole_mt_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import threading
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(40)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(32)(inputs["states"]))
x = nn.elu(nn.Dense(32)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(32)(inputs["states"]))
x = nn.elu(nn.Dense(32)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the multi-threaded Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Cartpole", multi_threaded=True, timeout=30)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 1 # 16 * 512 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 16
cfg["experiment"]["checkpoint_interval"] = 80
cfg["experiment"]["directory"] = "runs/jax/Cartpole"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 1600, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training in a separate thread
threading.Thread(target=trainer.train).start()
# run the simulation in the main thread
env.run()
| 5,613 | Python | 37.452055 | 102 | 0.706574 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ball_balance_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU(),
nn.Linear(64, 32),
nn.ELU())
self.mean_layer = nn.Linear(32, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(32, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="BallBalance")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 8 # 16 * 4096 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 20
cfg["experiment"]["checkpoint_interval"] = 200
cfg["experiment"]["directory"] = "runs/torch/BallBalance"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 4000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-BallBalance-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,043 | Python | 37.8 | 102 | 0.670236 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_franka_cabinet_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="FrankaCabinet")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 8 # 16 * 4096 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 5e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 120
cfg["experiment"]["checkpoint_interval"] = 1200
cfg["experiment"]["directory"] = "runs/jax/FrankaCabinet"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 24000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,535 | Python | 37.985915 | 102 | 0.701716 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ant_mt_ppo.py | import threading
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU())
self.mean_layer = nn.Linear(64, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(64, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the multi-threaded Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", multi_threaded=True, timeout=30)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 4
cfg["mini_batches"] = 2 # 16 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 40
cfg["experiment"]["checkpoint_interval"] = 400
cfg["experiment"]["directory"] = "runs/torch/Ant"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 8000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training in a separate thread
threading.Thread(target=trainer.train).start()
# run the simulation in the main thread
env.run()
| 4,650 | Python | 37.122951 | 101 | 0.683871 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ant_ddpg.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixins
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=64)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=15625, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=0.5, device=device)
cfg["gradient_steps"] = 1
cfg["batch_size"] = 4096
cfg["discount_factor"] = 0.99
cfg["polyak"] = 0.005
cfg["actor_learning_rate"] = 5e-4
cfg["critic_learning_rate"] = 5e-4
cfg["random_timesteps"] = 80
cfg["learning_starts"] = 80
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/Ant"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 4,205 | Python | 39.057142 | 106 | 0.679429 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_ant_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 4
cfg["mini_batches"] = 2 # 16 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 40
cfg["experiment"]["checkpoint_interval"] = 400
cfg["experiment"]["directory"] = "runs/jax/Ant"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 8000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,513 | Python | 37.830986 | 102 | 0.700526 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_anymal_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU())
self.mean_layer = nn.Linear(64, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(64, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Anymal")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=24, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 24 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 3 # 24 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 120
cfg["experiment"]["checkpoint_interval"] = 1200
cfg["experiment"]["directory"] = "runs/torch/Anymal"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 24000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-Anymal-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 4,988 | Python | 37.376923 | 101 | 0.668805 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_ant_ddpg_td3_sac_sequential_unshared_memory.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.agents.torch.sac import SAC, SAC_DEFAULT_CONFIG
from skrl.agents.torch.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.noises.torch import GaussianNoise, OrnsteinUhlenbeckNoise
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class StochasticActor(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-5, max_log_std=2):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
def compute(self, inputs, role):
return self.net(inputs["states"]), self.log_std_parameter, {}
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=192)
env = wrap_env(env)
device = env.device
# instantiate memories as experience replay (unique for each agents).
# scopes (192 envs): DDPG 64, TD3 64 and SAC 64
memory_ddpg = RandomMemory(memory_size=15625, num_envs=64, device=device)
memory_td3 = RandomMemory(memory_size=15625, num_envs=64, device=device)
memory_sac = RandomMemory(memory_size=15625, num_envs=64, device=device)
# instantiate the agents' models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models_ddpg = {}
models_ddpg["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_ddpg["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_ddpg["critic"] = Critic(env.observation_space, env.action_space, device)
models_ddpg["target_critic"] = Critic(env.observation_space, env.action_space, device)
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models_td3 = {}
models_td3["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_td3["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models_td3["critic_1"] = Critic(env.observation_space, env.action_space, device)
models_td3["critic_2"] = Critic(env.observation_space, env.action_space, device)
models_td3["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models_td3["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# SAC requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#models
models_sac = {}
models_sac["policy"] = StochasticActor(env.observation_space, env.action_space, device, clip_actions=True)
models_sac["critic_1"] = Critic(env.observation_space, env.action_space, device)
models_sac["critic_2"] = Critic(env.observation_space, env.action_space, device)
models_sac["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models_sac["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# configure and instantiate the agents (visit their documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg_ddpg = DDPG_DEFAULT_CONFIG.copy()
cfg_ddpg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=0.5, device=device)
cfg_ddpg["gradient_steps"] = 1
cfg_ddpg["batch_size"] = 4096
cfg_ddpg["discount_factor"] = 0.99
cfg_ddpg["polyak"] = 0.005
cfg_ddpg["actor_learning_rate"] = 5e-4
cfg_ddpg["critic_learning_rate"] = 5e-4
cfg_ddpg["random_timesteps"] = 80
cfg_ddpg["learning_starts"] = 80
cfg_ddpg["state_preprocessor"] = RunningStandardScaler
cfg_ddpg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_ddpg["experiment"]["write_interval"] = 800
cfg_ddpg["experiment"]["checkpoint_interval"] = 8000
cfg_ddpg["experiment"]["directory"] = "runs/torch/Ant"
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg_td3 = TD3_DEFAULT_CONFIG.copy()
cfg_td3["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg_td3["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg_td3["smooth_regularization_clip"] = 0.5
cfg_td3["gradient_steps"] = 1
cfg_td3["batch_size"] = 4096
cfg_td3["discount_factor"] = 0.99
cfg_td3["polyak"] = 0.005
cfg_td3["actor_learning_rate"] = 5e-4
cfg_td3["critic_learning_rate"] = 5e-4
cfg_td3["random_timesteps"] = 80
cfg_td3["learning_starts"] = 80
cfg_td3["state_preprocessor"] = RunningStandardScaler
cfg_td3["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_td3["experiment"]["write_interval"] = 800
cfg_td3["experiment"]["checkpoint_interval"] = 8000
cfg_td3["experiment"]["directory"] = "runs/torch/Ant"
# https://skrl.readthedocs.io/en/latest/api/agents/sac.html#configuration-and-hyperparameters
cfg_sac = SAC_DEFAULT_CONFIG.copy()
cfg_sac["gradient_steps"] = 1
cfg_sac["batch_size"] = 4096
cfg_sac["discount_factor"] = 0.99
cfg_sac["polyak"] = 0.005
cfg_sac["actor_learning_rate"] = 5e-4
cfg_sac["critic_learning_rate"] = 5e-4
cfg_sac["random_timesteps"] = 80
cfg_sac["learning_starts"] = 80
cfg_sac["grad_norm_clip"] = 0
cfg_sac["learn_entropy"] = True
cfg_sac["entropy_learning_rate"] = 5e-3
cfg_sac["initial_entropy_value"] = 1.0
cfg_sac["state_preprocessor"] = RunningStandardScaler
cfg_sac["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg_sac["experiment"]["write_interval"] = 800
cfg_sac["experiment"]["checkpoint_interval"] = 8000
cfg_sac["experiment"]["directory"] = "runs/torch/Ant"
agent_ddpg = DDPG(models=models_ddpg,
memory=memory_ddpg,
cfg=cfg_ddpg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
agent_td3 = TD3(models=models_td3,
memory=memory_td3,
cfg=cfg_td3,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
agent_sac = SAC(models=models_sac,
memory=memory_sac,
cfg=cfg_sac,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer and define the agent scopes
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer,
env=env,
agents=[agent_ddpg, agent_td3, agent_sac],
agents_scope=[64, 64, 64]) # scopes (192 envs): DDPG 64, TD3 64 and SAC 64
# start training
trainer.train()
| 9,360 | Python | 44.887255 | 111 | 0.676389 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_ant_td3.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, Model
from skrl.resources.noises.jax import GaussianNoise
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixins
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(512)(inputs["states"]))
x = nn.relu(nn.Dense(256)(x))
x = nn.Dense(self.num_actions)(x)
return nn.tanh(x), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(512)(x))
x = nn.relu(nn.Dense(256)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Ant", num_envs=64)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=15625, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# TD3 requires 6 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["gradient_steps"] = 1
cfg["batch_size"] = 4096
cfg["discount_factor"] = 0.99
cfg["polyak"] = 0.005
cfg["actor_learning_rate"] = 5e-4
cfg["critic_learning_rate"] = 5e-4
cfg["random_timesteps"] = 80
cfg["learning_starts"] = 80
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/jax/Ant"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,331 | Python | 38.791044 | 102 | 0.7145 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_cartpole_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(40)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 32),
nn.ELU(),
nn.Linear(32, 32),
nn.ELU())
self.mean_layer = nn.Linear(32, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(32, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Cartpole")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 1 # 16 * 512 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 16
cfg["experiment"]["checkpoint_interval"] = 80
cfg["experiment"]["directory"] = "runs/torch/Cartpole"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 1600, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-Cartpole-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 4,935 | Python | 37.5625 | 101 | 0.678825 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/torch_quadcopter_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_omniverse_isaacgym_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 256),
nn.ELU(),
nn.Linear(256, 256),
nn.ELU(),
nn.Linear(256, 128),
nn.ELU())
self.mean_layer = nn.Linear(128, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(128, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Quadcopter")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 4 # 16 * 4096 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 80
cfg["experiment"]["checkpoint_interval"] = 800
cfg["experiment"]["directory"] = "runs/torch/Quadcopter"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 16000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/OmniIsaacGymEnvs-Quadcopter-PPO", filename="agent.pt")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,047 | Python | 37.830769 | 101 | 0.670497 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_factory_task_nut_bolt_pick_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(256)(inputs["states"]))
x = nn.elu(nn.Dense(128)(x))
x = nn.elu(nn.Dense(64)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="FactoryTaskNutBoltPick")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=240, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 240 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 60 # 240 * 128 / 512
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-4
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0.016
cfg["rewards_shaper"] = None
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 614
cfg["experiment"]["checkpoint_interval"] = 6144
cfg["experiment"]["directory"] = "runs/jax/FactoryTaskNutBoltPick"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 120000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,346 | Python | 37.467626 | 102 | 0.699963 |
Toni-SM/skrl/docs/source/examples/omniisaacgym/jax_crazyflie_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_omniverse_isaacgym_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.tanh(nn.Dense(256)(inputs["states"]))
x = nn.tanh(nn.Dense(256)(x))
x = nn.tanh(nn.Dense(128)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.zeros(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.tanh(nn.Dense(256)(inputs["states"]))
x = nn.tanh(nn.Dense(256)(x))
x = nn.tanh(nn.Dense(128)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Omniverse Isaac Gym environment
env = load_omniverse_isaacgym_env(task_name="Crazyflie")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 4 # 16 * 4096 / 16384
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 80
cfg["experiment"]["checkpoint_interval"] = 800
cfg["experiment"]["directory"] = "runs/jax/Crazyflie"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 16000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 5,534 | Python | 37.978873 | 102 | 0.701662 |
Toni-SM/skrl/docs/source/examples/shimmy/torch_shimmy_dm_control_acrobot_swingup_sparse_sac.py | import gymnasium as gym
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
return torch.tanh(self.action_layer(x)), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# load and wrap the environment
env = gym.make("dm_control/acrobot-swingup_sparse-v0")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 1000
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/dm_control_acrobot_swingup_sparse"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,938 | Python | 37.242718 | 106 | 0.7161 |
Toni-SM/skrl/docs/source/examples/shimmy/jax_shimmy_openai_gym_compatibility_pendulum_ddpg.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, Model
from skrl.resources.noises.jax import OrnsteinUhlenbeckNoise
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(400)(inputs["states"]))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(self.num_actions)(x)
# Pendulum-v1 action_space is -2 to 2
return 2 * nn.tanh(x), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(400)(x))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the environment
env = gym.make("GymV21Environment-v0", env_id="Pendulum-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=15000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["batch_size"] = 100
cfg["random_timesteps"] = 100
cfg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 300
cfg["experiment"]["checkpoint_interval"] = 1500
cfg["experiment"]["directory"] = "runs/torch/GymV21Environment_Pendulum"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,904 | Python | 35.157407 | 106 | 0.710041 |
Toni-SM/skrl/docs/source/examples/shimmy/torch_shimmy_atari_pong_dqn.py | import gymnasium as gym
import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.dqn import DQN, DQN_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define model (deterministic model) using mixin
class QNetwork(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, self.num_actions))
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
# load and wrap the environment
env = gym.make("ALE/Pong-v5")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=15000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DQN requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#models
models = {}
models["q_network"] = QNetwork(env.observation_space, env.action_space, device)
models["target_q_network"] = QNetwork(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#configuration-and-hyperparameters
cfg = DQN_DEFAULT_CONFIG.copy()
cfg["learning_starts"] = 100
cfg["exploration"]["initial_epsilon"] = 1.0
cfg["exploration"]["final_epsilon"] = 0.04
cfg["exploration"]["timesteps"] = 1500
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/ALE_Pong"
agent = DQN(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 50000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 2,898 | Python | 33.511904 | 97 | 0.695997 |
Toni-SM/skrl/docs/source/examples/shimmy/jax_shimmy_atari_pong_dqn.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.dqn import DQN, DQN_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, Model
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define model (deterministic model) using mixin
class QNetwork(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(64)(inputs["states"]))
x = nn.relu(nn.Dense(64)(x))
x = nn.Dense(self.num_actions)(x)
return x, {}
# load and wrap the environment
env = gym.make("ALE/Pong-v5")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=15000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DQN requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#models
models = {}
models["q_network"] = QNetwork(env.observation_space, env.action_space, device)
models["target_q_network"] = QNetwork(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/dqn.html#configuration-and-hyperparameters
cfg = DQN_DEFAULT_CONFIG.copy()
cfg["learning_starts"] = 100
cfg["exploration"]["final_epsilon"] = 0.04
cfg["exploration"]["timesteps"] = 1500
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 1000
cfg["experiment"]["checkpoint_interval"] = 5000
cfg["experiment"]["directory"] = "runs/torch/ALE_Pong"
agent = DQN(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 50000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 2,898 | Python | 31.211111 | 99 | 0.720497 |
Toni-SM/skrl/docs/source/examples/shimmy/torch_shimmy_openai_gym_compatibility_pendulum_ddpg.py | import gymnasium as gym
import torch
import torch.nn as nn
import torch.nn.functional as F
# import the skrl components to build the RL system
from skrl.agents.torch.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.action_layer = nn.Linear(300, self.num_actions)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(inputs["states"]))
x = F.relu(self.linear_layer_2(x))
# Pendulum-v1 action_space is -2 to 2
return 2 * torch.tanh(self.action_layer(x)), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.linear_layer_1 = nn.Linear(self.num_observations + self.num_actions, 400)
self.linear_layer_2 = nn.Linear(400, 300)
self.linear_layer_3 = nn.Linear(300, 1)
def compute(self, inputs, role):
x = F.relu(self.linear_layer_1(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)))
x = F.relu(self.linear_layer_2(x))
return self.linear_layer_3(x), {}
# load and wrap the environment
env = gym.make("GymV21Environment-v0", env_id="Pendulum-v1")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=15000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal_", mean=0.0, std=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["batch_size"] = 100
cfg["random_timesteps"] = 100
cfg["learning_starts"] = 100
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 300
cfg["experiment"]["checkpoint_interval"] = 1500
cfg["experiment"]["directory"] = "runs/torch/GymV21Environment_Pendulum"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,957 | Python | 37.427184 | 106 | 0.71443 |
Toni-SM/skrl/docs/source/examples/shimmy/jax_shimmy_dm_control_acrobot_swingup_sparse_sac.py | import gymnasium as gym
import flax.linen as nn
import jax
import jax.numpy as jnp
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ddpg import DDPG, DDPG_DEFAULT_CONFIG
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, Model
from skrl.resources.noises.jax import OrnsteinUhlenbeckNoise
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "numpy" # or "jax"
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixin
class Actor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact
def __call__(self, inputs, role):
x = nn.relu(nn.Dense(400)(inputs["states"]))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(self.num_actions)(x)
return nn.tanh(x), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = jnp.concatenate([inputs["states"], inputs["taken_actions"]], axis=-1)
x = nn.relu(nn.Dense(400)(x))
x = nn.relu(nn.Dense(300)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the environment
env = gym.make("dm_control/acrobot-swingup_sparse-v0")
env = wrap_env(env)
device = env.device
# instantiate a memory as experience replay
memory = RandomMemory(memory_size=20000, num_envs=env.num_envs, device=device, replacement=False)
# instantiate the agent's models (function approximators).
# DDPG requires 4 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#models
models = {}
models["policy"] = Actor(env.observation_space, env.action_space, device)
models["target_policy"] = Actor(env.observation_space, env.action_space, device)
models["critic"] = Critic(env.observation_space, env.action_space, device)
models["target_critic"] = Critic(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# initialize models' parameters (weights and biases)
for model in models.values():
model.init_parameters(method_name="normal", stddev=0.1)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ddpg.html#configuration-and-hyperparameters
cfg = DDPG_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.1, base_scale=1.0, device=device)
cfg["discount_factor"] = 0.98
cfg["batch_size"] = 100
cfg["random_timesteps"] = 1000
cfg["learning_starts"] = 1000
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 75
cfg["experiment"]["checkpoint_interval"] = 750
cfg["experiment"]["directory"] = "runs/torch/dm_control_acrobot_swingup_sparse"
agent = DDPG(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 15000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=[agent])
# start training
trainer.train()
| 3,885 | Python | 34.981481 | 106 | 0.711712 |
Toni-SM/skrl/docs/source/examples/utils/tensorboard_file_iterator.py | import numpy as np
import matplotlib.pyplot as plt
from skrl.utils import postprocessing
labels = []
rewards = []
# load the Tensorboard files and iterate over them (tag: "Reward / Total reward (mean)")
tensorboard_iterator = postprocessing.TensorboardFileIterator("runs/*/events.out.tfevents.*",
tags=["Reward / Total reward (mean)"])
for dirname, data in tensorboard_iterator:
rewards.append(data["Reward / Total reward (mean)"])
labels.append(dirname)
# convert to numpy arrays and compute mean and std
rewards = np.array(rewards)
mean = np.mean(rewards[:,:,1], axis=0)
std = np.std(rewards[:,:,1], axis=0)
# creae two subplots (one for each reward and one for the mean)
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
# plot the rewards for each experiment
for reward, label in zip(rewards, labels):
ax[0].plot(reward[:,0], reward[:,1], label=label)
ax[0].set_title("Total reward (for each experiment)")
ax[0].set_xlabel("Timesteps")
ax[0].set_ylabel("Reward")
ax[0].grid(True)
ax[0].legend()
# plot the mean and std (across experiments)
ax[1].fill_between(rewards[0,:,0], mean - std, mean + std, alpha=0.5, label="std")
ax[1].plot(rewards[0,:,0], mean, label="mean")
ax[1].set_title("Total reward (mean and std of all experiments)")
ax[1].set_xlabel("Timesteps")
ax[1].set_ylabel("Reward")
ax[1].grid(True)
ax[1].legend()
# show and save the figure
plt.show()
plt.savefig("total_reward.png")
| 1,480 | Python | 29.854166 | 100 | 0.670946 |
Toni-SM/skrl/docs/source/examples/isaacorbit/torch_ant_td3.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.td3 import TD3, TD3_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_isaac_orbit_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, Model
from skrl.resources.noises.torch import GaussianNoise
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define models (deterministic models) using mixins
class DeterministicActor(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, self.num_actions),
nn.Tanh())
def compute(self, inputs, role):
return self.net(inputs["states"]), {}
class Critic(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False):
Model.__init__(self, observation_space, action_space, device)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations + self.num_actions, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 1))
def compute(self, inputs, role):
return self.net(torch.cat([inputs["states"], inputs["taken_actions"]], dim=1)), {}
# load and wrap the Isaac Orbit environment
env = load_isaac_orbit_env(task_name="Isaac-Ant-v0", num_envs=64)
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=15625, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# TD3 requires 5 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#models
models = {}
models["policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["target_policy"] = DeterministicActor(env.observation_space, env.action_space, device)
models["critic_1"] = Critic(env.observation_space, env.action_space, device)
models["critic_2"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_1"] = Critic(env.observation_space, env.action_space, device)
models["target_critic_2"] = Critic(env.observation_space, env.action_space, device)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/td3.html#configuration-and-hyperparameters
cfg = TD3_DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = GaussianNoise(0, 0.1, device=device)
cfg["smooth_regularization_noise"] = GaussianNoise(0, 0.2, device=device)
cfg["smooth_regularization_clip"] = 0.5
cfg["gradient_steps"] = 1
cfg["batch_size"] = 4096
cfg["discount_factor"] = 0.99
cfg["polyak"] = 0.005
cfg["actor_learning_rate"] = 5e-4
cfg["critic_learning_rate"] = 5e-4
cfg["random_timesteps"] = 80
cfg["learning_starts"] = 80
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 800
cfg["experiment"]["checkpoint_interval"] = 8000
cfg["experiment"]["directory"] = "runs/torch/Isaac-Ant-v0"
agent = TD3(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 160000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 4,418 | Python | 39.541284 | 93 | 0.681304 |
Toni-SM/skrl/docs/source/examples/isaacorbit/torch_velocity_anymal_c_ppo.py | import torch
import torch.nn as nn
# import the skrl components to build the RL system
from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.torch import load_isaac_orbit_env
from skrl.envs.wrappers.torch import wrap_env
from skrl.memories.torch import RandomMemory
from skrl.models.torch import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.torch import RunningStandardScaler
from skrl.resources.schedulers.torch import KLAdaptiveRL
from skrl.trainers.torch import SequentialTrainer
from skrl.utils import set_seed
# seed for reproducibility
set_seed() # e.g. `set_seed(42)` for fixed seed
# define shared model (stochastic and deterministic models) using mixins
class Shared(GaussianMixin, DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum"):
Model.__init__(self, observation_space, action_space, device)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
DeterministicMixin.__init__(self, clip_actions)
self.net = nn.Sequential(nn.Linear(self.num_observations, 128),
nn.ELU(),
nn.Linear(128, 128),
nn.ELU(),
nn.Linear(128, 128),
nn.ELU())
self.mean_layer = nn.Linear(128, self.num_actions)
self.log_std_parameter = nn.Parameter(torch.zeros(self.num_actions))
self.value_layer = nn.Linear(128, 1)
def act(self, inputs, role):
if role == "policy":
return GaussianMixin.act(self, inputs, role)
elif role == "value":
return DeterministicMixin.act(self, inputs, role)
def compute(self, inputs, role):
if role == "policy":
return self.mean_layer(self.net(inputs["states"])), self.log_std_parameter, {}
elif role == "value":
return self.value_layer(self.net(inputs["states"])), {}
# load and wrap the Isaac Orbit environment
env = load_isaac_orbit_env(task_name="Isaac-Velocity-Anymal-C-v0")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=24, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Shared(env.observation_space, env.action_space, device)
models["value"] = models["policy"] # same instance: shared model
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 24 # memory_size
cfg["learning_epochs"] = 5
cfg["mini_batches"] = 4 # 24 * 4096 / 24576
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 1e-3
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.01}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = None
cfg["time_limit_bootstrap"] = False
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 60
cfg["experiment"]["checkpoint_interval"] = 600
cfg["experiment"]["directory"] = "runs/torch/Isaac-Velocity-Anymal-C-v0"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 12000, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
| 4,496 | Python | 37.435897 | 101 | 0.679715 |
Toni-SM/skrl/docs/source/examples/isaacorbit/jax_cartpole_ppo.py | """
Notes for Isaac Sim 2022.2.1 or earlier (Python 3.7 environment):
* Python 3.7 is only supported up to jax<=0.3.25.
See: https://github.com/google/jax/blob/main/CHANGELOG.md#jaxlib-041-dec-13-2022.
* Builds for jaxlib<=0.3.25 are only available up to NVIDIA CUDA 11 and cuDNN 8.2 versions.
See: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
and search for `cuda11/jaxlib-0.3.25+cuda11.cudnn82-cp37-cp37m-manylinux2014_x86_64.whl`.
* The `jax.Device = jax.xla.Device` statement is required by skrl to support jax<0.4.3.
* Models require overloading the `__hash__` method to avoid "TypeError: Failed to hash Flax Module".
"""
import flax.linen as nn
import jax
import jax.numpy as jnp
jax.Device = jax.xla.Device # for Isaac Sim 2022.2.1 or earlier
# import the skrl components to build the RL system
from skrl import config
from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
from skrl.envs.loaders.jax import load_isaac_orbit_env
from skrl.envs.wrappers.jax import wrap_env
from skrl.memories.jax import RandomMemory
from skrl.models.jax import DeterministicMixin, GaussianMixin, Model
from skrl.resources.preprocessors.jax import RunningStandardScaler
from skrl.resources.schedulers.jax import KLAdaptiveRL
from skrl.trainers.jax import SequentialTrainer
from skrl.utils import set_seed
config.jax.backend = "jax" # or "numpy"
# seed for reproducibility
set_seed() # e.g. `set_seed(40)` for fixed seed
# define models (stochastic and deterministic models) using mixins
class Policy(GaussianMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False,
clip_log_std=True, min_log_std=-20, max_log_std=2, reduction="sum", **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
GaussianMixin.__init__(self, clip_actions, clip_log_std, min_log_std, max_log_std, reduction)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(32)(inputs["states"]))
x = nn.elu(nn.Dense(32)(x))
x = nn.Dense(self.num_actions)(x)
log_std = self.param("log_std", lambda _: jnp.ones(self.num_actions))
return x, log_std, {}
class Value(DeterministicMixin, Model):
def __init__(self, observation_space, action_space, device=None, clip_actions=False, **kwargs):
Model.__init__(self, observation_space, action_space, device, **kwargs)
DeterministicMixin.__init__(self, clip_actions)
def __hash__(self): # for Isaac Sim 2022.2.1 or earlier
return id(self)
@nn.compact # marks the given module method allowing inlined submodules
def __call__(self, inputs, role):
x = nn.elu(nn.Dense(32)(inputs["states"]))
x = nn.elu(nn.Dense(32)(x))
x = nn.Dense(1)(x)
return x, {}
# load and wrap the Isaac Orbit environment
env = load_isaac_orbit_env(task_name="Isaac-Cartpole-v0")
env = wrap_env(env)
device = env.device
# instantiate a memory as rollout buffer (any memory can be used for this)
memory = RandomMemory(memory_size=16, num_envs=env.num_envs, device=device)
# instantiate the agent's models (function approximators).
# PPO requires 2 models, visit its documentation for more details
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#models
models = {}
models["policy"] = Policy(env.observation_space, env.action_space, device)
models["value"] = Value(env.observation_space, env.action_space, device)
# instantiate models' state dict
for role, model in models.items():
model.init_state_dict(role)
# configure and instantiate the agent (visit its documentation to see all the options)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 1 # 16 * 512 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = None
cfg["time_limit_bootstrap"] = True
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
# logging to TensorBoard and write checkpoints (in timesteps)
cfg["experiment"]["write_interval"] = 16
cfg["experiment"]["checkpoint_interval"] = 80
cfg["experiment"]["directory"] = "runs/jax/Isaac-Cartpole-v0"
agent = PPO(models=models,
memory=memory,
cfg=cfg,
observation_space=env.observation_space,
action_space=env.action_space,
device=device)
# configure and instantiate the RL trainer
cfg_trainer = {"timesteps": 1600, "headless": True}
trainer = SequentialTrainer(cfg=cfg_trainer, env=env, agents=agent)
# start training
trainer.train()
# # ---------------------------------------------------------
# # comment the code above: `trainer.train()`, and...
# # uncomment the following lines to evaluate a trained agent
# # ---------------------------------------------------------
# from skrl.utils.huggingface import download_model_from_huggingface
# # download the trained agent's checkpoint from Hugging Face Hub and load it
# path = download_model_from_huggingface("skrl/IsaacOrbit-Isaac-Cartpole-v0-PPO", filename="agent.pickle")
# agent.load(path)
# # start evaluation
# trainer.eval()
| 5,982 | Python | 37.6 | 106 | 0.691742 |
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