File size: 11,201 Bytes
05c9ac2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 |
from typing import cast
from mlagents.torch_utils import torch, nn, default_device
from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
from mlagents.trainers.policy.torch_policy import TorchPolicy
from mlagents.trainers.buffer import AgentBuffer, BufferKey, RewardSignalUtil
from mlagents_envs.timers import timed
from typing import List, Dict, Tuple, Optional, Union, Any
from mlagents.trainers.torch_entities.networks import ValueNetwork, Actor
from mlagents_envs.base_env import ActionSpec, ObservationSpec
from mlagents.trainers.torch_entities.agent_action import AgentAction
from mlagents.trainers.torch_entities.utils import ModelUtils
from mlagents.trainers.trajectory import ObsUtil
from mlagents.trainers.settings import TrainerSettings, OffPolicyHyperparamSettings
from mlagents.trainers.settings import ScheduleType, NetworkSettings
from mlagents.trainers.torch_entities.networks import Critic
import numpy as np
import attr
# TODO: fix saving to onnx
@attr.s(auto_attribs=True)
class DQNSettings(OffPolicyHyperparamSettings):
gamma: float = 0.99
exploration_schedule: ScheduleType = ScheduleType.LINEAR
exploration_initial_eps: float = 0.1
exploration_final_eps: float = 0.05
target_update_interval: int = 10000
tau: float = 0.005
steps_per_update: float = 1
save_replay_buffer: bool = False
reward_signal_steps_per_update: float = attr.ib()
@reward_signal_steps_per_update.default
def _reward_signal_steps_per_update_default(self):
return self.steps_per_update
class DQNOptimizer(TorchOptimizer):
def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
super().__init__(policy, trainer_settings)
# initialize hyper parameters
params = list(self.policy.actor.parameters())
self.optimizer = torch.optim.Adam(
params, lr=self.trainer_settings.hyperparameters.learning_rate
)
self.stream_names = list(self.reward_signals.keys())
self.gammas = [_val.gamma for _val in trainer_settings.reward_signals.values()]
self.use_dones_in_backup = {
name: int(not self.reward_signals[name].ignore_done)
for name in self.stream_names
}
self.hyperparameters: DQNSettings = cast(
DQNSettings, trainer_settings.hyperparameters
)
self.tau = self.hyperparameters.tau
self.decay_learning_rate = ModelUtils.DecayedValue(
self.hyperparameters.learning_rate_schedule,
self.hyperparameters.learning_rate,
1e-10,
self.trainer_settings.max_steps,
)
self.decay_exploration_rate = ModelUtils.DecayedValue(
self.hyperparameters.exploration_schedule,
self.hyperparameters.exploration_initial_eps,
self.hyperparameters.exploration_final_eps,
20000,
)
# initialize Target Q_network
self.q_net_target = QNetwork(
stream_names=self.reward_signals.keys(),
observation_specs=policy.behavior_spec.observation_specs,
network_settings=policy.network_settings,
action_spec=policy.behavior_spec.action_spec,
)
ModelUtils.soft_update(self.policy.actor, self.q_net_target, 1.0)
self.q_net_target.to(default_device())
@property
def critic(self):
return self.q_net_target
@timed
def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
"""
Performs update on model.
:param batch: Batch of experiences.
:param num_sequences: Number of sequences to process.
:return: Results of update.
"""
# Get decayed parameters
decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
exp_rate = self.decay_exploration_rate.get_value(self.policy.get_current_step())
self.policy.actor.exploration_rate = exp_rate
rewards = {}
for name in self.reward_signals:
rewards[name] = ModelUtils.list_to_tensor(
batch[RewardSignalUtil.rewards_key(name)]
)
n_obs = len(self.policy.behavior_spec.observation_specs)
current_obs = ObsUtil.from_buffer(batch, n_obs)
# Convert to tensors
current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
next_obs = ObsUtil.from_buffer_next(batch, n_obs)
# Convert to tensors
next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
actions = AgentAction.from_buffer(batch)
dones = ModelUtils.list_to_tensor(batch[BufferKey.DONE])
current_q_values, _ = self.policy.actor.critic_pass(
current_obs, sequence_length=self.policy.sequence_length
)
qloss = []
with torch.no_grad():
greedy_actions = self.policy.actor.get_greedy_action(current_q_values)
next_q_values_list, _ = self.q_net_target.critic_pass(
next_obs, sequence_length=self.policy.sequence_length
)
for name_i, name in enumerate(rewards.keys()):
with torch.no_grad():
next_q_values = torch.gather(
next_q_values_list[name], dim=1, index=greedy_actions
).squeeze()
target_q_values = rewards[name] + (
(1.0 - self.use_dones_in_backup[name] * dones)
* self.gammas[name_i]
* next_q_values
)
target_q_values = target_q_values.reshape(-1, 1)
curr_q = torch.gather(
current_q_values[name], dim=1, index=actions.discrete_tensor
)
qloss.append(torch.nn.functional.smooth_l1_loss(curr_q, target_q_values))
loss = torch.mean(torch.stack(qloss))
ModelUtils.update_learning_rate(self.optimizer, decay_lr)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
ModelUtils.soft_update(self.policy.actor, self.q_net_target, self.tau)
update_stats = {
"Losses/Value Loss": loss.item(),
"Policy/Learning Rate": decay_lr,
"Policy/epsilon": exp_rate,
}
for reward_provider in self.reward_signals.values():
update_stats.update(reward_provider.update(batch))
return update_stats
def get_modules(self):
modules = {
"Optimizer:value_optimizer": self.optimizer,
"Optimizer:critic": self.critic,
}
for reward_provider in self.reward_signals.values():
modules.update(reward_provider.get_modules())
return modules
class QNetwork(nn.Module, Actor, Critic):
MODEL_EXPORT_VERSION = 3
def __init__(
self,
stream_names: List[str],
observation_specs: List[ObservationSpec],
network_settings: NetworkSettings,
action_spec: ActionSpec,
exploration_initial_eps: float = 1.0,
):
self.exploration_rate = exploration_initial_eps
nn.Module.__init__(self)
output_act_size = max(sum(action_spec.discrete_branches), 1)
self.network_body = ValueNetwork(
stream_names,
observation_specs,
network_settings,
outputs_per_stream=output_act_size,
)
# extra tensors for exporting to ONNX
self.action_spec = action_spec
self.version_number = torch.nn.Parameter(
torch.Tensor([self.MODEL_EXPORT_VERSION]), requires_grad=False
)
self.is_continuous_int_deprecated = torch.nn.Parameter(
torch.Tensor([int(self.action_spec.is_continuous())]), requires_grad=False
)
self.continuous_act_size_vector = torch.nn.Parameter(
torch.Tensor([int(self.action_spec.continuous_size)]), requires_grad=False
)
self.discrete_act_size_vector = torch.nn.Parameter(
torch.Tensor([self.action_spec.discrete_branches]), requires_grad=False
)
self.act_size_vector_deprecated = torch.nn.Parameter(
torch.Tensor(
[
self.action_spec.continuous_size
+ sum(self.action_spec.discrete_branches)
]
),
requires_grad=False,
)
self.memory_size_vector = torch.nn.Parameter(
torch.Tensor([int(self.network_body.memory_size)]), requires_grad=False
)
def update_normalization(self, buffer: AgentBuffer) -> None:
self.network_body.update_normalization(buffer)
def critic_pass(
self,
inputs: List[torch.Tensor],
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
value_outputs, critic_mem_out = self.network_body(
inputs, memories=memories, sequence_length=sequence_length
)
return value_outputs, critic_mem_out
@property
def memory_size(self) -> int:
return self.network_body.memory_size
def forward(
self,
inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[Union[int, torch.Tensor], ...]:
out_vals, memories = self.critic_pass(inputs, memories, sequence_length)
# fixme random action tensor
export_out = [self.version_number, self.memory_size_vector]
disc_action_out = self.get_greedy_action(out_vals)
deterministic_disc_action_out = self.get_random_action(out_vals)
export_out += [
disc_action_out,
self.discrete_act_size_vector,
deterministic_disc_action_out,
]
return tuple(export_out)
def get_random_action(self, inputs) -> torch.Tensor:
action_out = torch.randint(
0, self.action_spec.discrete_branches[0], (len(inputs), 1)
)
return action_out
@staticmethod
def get_greedy_action(q_values) -> torch.Tensor:
all_q = torch.cat([val.unsqueeze(0) for val in q_values.values()])
return torch.argmax(all_q.sum(dim=0), dim=1, keepdim=True)
def get_action_and_stats(
self,
inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
deterministic=False,
) -> Tuple[AgentAction, Dict[str, Any], torch.Tensor]:
run_out = {}
if not deterministic and np.random.rand() < self.exploration_rate:
action_out = self.get_random_action(inputs)
action_out = AgentAction(None, [action_out])
run_out["env_action"] = action_out.to_action_tuple()
else:
out_vals, _ = self.critic_pass(inputs, memories, sequence_length)
action_out = self.get_greedy_action(out_vals)
action_out = AgentAction(None, [action_out])
run_out["env_action"] = action_out.to_action_tuple()
return action_out, run_out, torch.Tensor([])
|