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# # Unity ML-Agents Toolkit
# ## ML-Agent Learning
"""Launches trainers for each External Brains in a Unity Environment."""
import os
import threading
from typing import Dict, Set, List
from collections import defaultdict
import numpy as np
from mlagents_envs.logging_util import get_logger
from mlagents.trainers.env_manager import EnvManager, EnvironmentStep
from mlagents_envs.exception import (
UnityEnvironmentException,
UnityCommunicationException,
UnityCommunicatorStoppedException,
)
from mlagents_envs.timers import (
hierarchical_timer,
timed,
get_timer_stack_for_thread,
merge_gauges,
)
from mlagents.trainers.trainer import Trainer
from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
from mlagents.trainers.trainer import TrainerFactory
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
from mlagents.trainers.agent_processor import AgentManager
from mlagents import torch_utils
from mlagents.torch_utils.globals import get_rank
class TrainerController:
def __init__(
self,
trainer_factory: TrainerFactory,
output_path: str,
run_id: str,
param_manager: EnvironmentParameterManager,
train: bool,
training_seed: int,
):
"""
:param output_path: Path to save the model.
:param summaries_dir: Folder to save training summaries.
:param run_id: The sub-directory name for model and summary statistics
:param param_manager: EnvironmentParameterManager object which stores information about all
environment parameters.
:param train: Whether to train model, or only run inference.
:param training_seed: Seed to use for Numpy and Torch random number generation.
:param threaded: Whether or not to run trainers in a separate thread. Disable for testing/debugging.
"""
self.trainers: Dict[str, Trainer] = {}
self.brain_name_to_identifier: Dict[str, Set] = defaultdict(set)
self.trainer_factory = trainer_factory
self.output_path = output_path
self.logger = get_logger(__name__)
self.run_id = run_id
self.train_model = train
self.param_manager = param_manager
self.ghost_controller = self.trainer_factory.ghost_controller
self.registered_behavior_ids: Set[str] = set()
self.trainer_threads: List[threading.Thread] = []
self.kill_trainers = False
np.random.seed(training_seed)
torch_utils.torch.manual_seed(training_seed)
self.rank = get_rank()
@timed
def _save_models(self):
"""
Saves current model to checkpoint folder.
"""
if self.rank is not None and self.rank != 0:
return
for brain_name in self.trainers.keys():
self.trainers[brain_name].save_model()
self.logger.debug("Saved Model")
@staticmethod
def _create_output_path(output_path):
try:
if not os.path.exists(output_path):
os.makedirs(output_path)
except Exception:
raise UnityEnvironmentException(
f"The folder {output_path} containing the "
"generated model could not be "
"accessed. Please make sure the "
"permissions are set correctly."
)
@timed
def _reset_env(self, env_manager: EnvManager) -> None:
"""Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment.
"""
new_config = self.param_manager.get_current_samplers()
env_manager.reset(config=new_config)
# Register any new behavior ids that were generated on the reset.
self._register_new_behaviors(env_manager, env_manager.first_step_infos)
def _not_done_training(self) -> bool:
return (
any(t.should_still_train for t in self.trainers.values())
or not self.train_model
) or len(self.trainers) == 0
def _create_trainer_and_manager(
self, env_manager: EnvManager, name_behavior_id: str
) -> None:
parsed_behavior_id = BehaviorIdentifiers.from_name_behavior_id(name_behavior_id)
brain_name = parsed_behavior_id.brain_name
trainerthread = None
if brain_name in self.trainers:
trainer = self.trainers[brain_name]
else:
trainer = self.trainer_factory.generate(brain_name)
self.trainers[brain_name] = trainer
if trainer.threaded:
# Only create trainer thread for new trainers
trainerthread = threading.Thread(
target=self.trainer_update_func, args=(trainer,), daemon=True
)
self.trainer_threads.append(trainerthread)
env_manager.on_training_started(
brain_name, self.trainer_factory.trainer_config[brain_name]
)
policy = trainer.create_policy(
parsed_behavior_id,
env_manager.training_behaviors[name_behavior_id],
)
trainer.add_policy(parsed_behavior_id, policy)
agent_manager = AgentManager(
policy,
name_behavior_id,
trainer.stats_reporter,
trainer.parameters.time_horizon,
threaded=trainer.threaded,
)
env_manager.set_agent_manager(name_behavior_id, agent_manager)
env_manager.set_policy(name_behavior_id, policy)
self.brain_name_to_identifier[brain_name].add(name_behavior_id)
trainer.publish_policy_queue(agent_manager.policy_queue)
trainer.subscribe_trajectory_queue(agent_manager.trajectory_queue)
# Only start new trainers
if trainerthread is not None:
trainerthread.start()
def _create_trainers_and_managers(
self, env_manager: EnvManager, behavior_ids: Set[str]
) -> None:
for behavior_id in behavior_ids:
self._create_trainer_and_manager(env_manager, behavior_id)
@timed
def start_learning(self, env_manager: EnvManager) -> None:
self._create_output_path(self.output_path)
try:
# Initial reset
self._reset_env(env_manager)
self.param_manager.log_current_lesson()
while self._not_done_training():
n_steps = self.advance(env_manager)
for _ in range(n_steps):
self.reset_env_if_ready(env_manager)
# Stop advancing trainers
self.join_threads()
except (
KeyboardInterrupt,
UnityCommunicationException,
UnityEnvironmentException,
UnityCommunicatorStoppedException,
) as ex:
self.join_threads()
self.logger.info(
"Learning was interrupted. Please wait while the graph is generated."
)
if isinstance(ex, KeyboardInterrupt) or isinstance(
ex, UnityCommunicatorStoppedException
):
pass
else:
# If the environment failed, we want to make sure to raise
# the exception so we exit the process with an return code of 1.
raise ex
finally:
if self.train_model:
self._save_models()
def end_trainer_episodes(self) -> None:
# Reward buffers reset takes place only for curriculum learning
# else no reset.
for trainer in self.trainers.values():
trainer.end_episode()
def reset_env_if_ready(self, env: EnvManager) -> None:
# Get the sizes of the reward buffers.
reward_buff = {k: list(t.reward_buffer) for (k, t) in self.trainers.items()}
curr_step = {k: int(t.get_step) for (k, t) in self.trainers.items()}
max_step = {k: int(t.get_max_steps) for (k, t) in self.trainers.items()}
# Attempt to increment the lessons of the brains who
# were ready.
updated, param_must_reset = self.param_manager.update_lessons(
curr_step, max_step, reward_buff
)
if updated:
for trainer in self.trainers.values():
trainer.reward_buffer.clear()
# If ghost trainer swapped teams
ghost_controller_reset = self.ghost_controller.should_reset()
if param_must_reset or ghost_controller_reset:
self._reset_env(env) # This reset also sends the new config to env
self.end_trainer_episodes()
elif updated:
env.set_env_parameters(self.param_manager.get_current_samplers())
@timed
def advance(self, env_manager: EnvManager) -> int:
# Get steps
with hierarchical_timer("env_step"):
new_step_infos = env_manager.get_steps()
self._register_new_behaviors(env_manager, new_step_infos)
num_steps = env_manager.process_steps(new_step_infos)
# Report current lesson for each environment parameter
for (
param_name,
lesson_number,
) in self.param_manager.get_current_lesson_number().items():
for trainer in self.trainers.values():
trainer.stats_reporter.set_stat(
f"Environment/Lesson Number/{param_name}", lesson_number
)
for trainer in self.trainers.values():
if not trainer.threaded:
with hierarchical_timer("trainer_advance"):
trainer.advance()
return num_steps
def _register_new_behaviors(
self, env_manager: EnvManager, step_infos: List[EnvironmentStep]
) -> None:
"""
Handle registration (adding trainers and managers) of new behaviors ids.
:param env_manager:
:param step_infos:
:return:
"""
step_behavior_ids: Set[str] = set()
for s in step_infos:
step_behavior_ids |= set(s.name_behavior_ids)
new_behavior_ids = step_behavior_ids - self.registered_behavior_ids
self._create_trainers_and_managers(env_manager, new_behavior_ids)
self.registered_behavior_ids |= step_behavior_ids
def join_threads(self, timeout_seconds: float = 1.0) -> None:
"""
Wait for threads to finish, and merge their timer information into the main thread.
:param timeout_seconds:
:return:
"""
self.kill_trainers = True
for t in self.trainer_threads:
try:
t.join(timeout_seconds)
except Exception:
pass
with hierarchical_timer("trainer_threads") as main_timer_node:
for trainer_thread in self.trainer_threads:
thread_timer_stack = get_timer_stack_for_thread(trainer_thread)
if thread_timer_stack:
main_timer_node.merge(
thread_timer_stack.root,
root_name="thread_root",
is_parallel=True,
)
merge_gauges(thread_timer_stack.gauges)
def trainer_update_func(self, trainer: Trainer) -> None:
while not self.kill_trainers:
with hierarchical_timer("trainer_advance"):
trainer.advance()
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