Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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# # Unity ML-Agents Toolkit
# ## ML-Agent Learning
"""Launches trainers for each External Brains in a Unity Environment."""
import os
import sys
from typing import Dict, Optional, Set
from collections import defaultdict
import numpy as np
from mlagents.tf_utils import tf
from mlagents_envs.logging_util import get_logger
from mlagents.trainers.env_manager import EnvManager
from mlagents_envs.exception import (
UnityEnvironmentException,
UnityCommunicationException,
)
from mlagents.trainers.sampler_class import SamplerManager
from mlagents_envs.timers import hierarchical_timer, timed
from mlagents.trainers.trainer import Trainer
from mlagents.trainers.meta_curriculum import MetaCurriculum
from mlagents.trainers.trainer_util import TrainerFactory
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
from mlagents.trainers.agent_processor import AgentManager
class TrainerController(object):
def __init__(
self,
trainer_factory: TrainerFactory,
model_path: str,
summaries_dir: str,
run_id: str,
save_freq: int,
meta_curriculum: Optional[MetaCurriculum],
train: bool,
training_seed: int,
sampler_manager: SamplerManager,
resampling_interval: Optional[int],
):
"""
:param model_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 save_freq: Frequency at which to save model
:param meta_curriculum: MetaCurriculum object which stores information about all curricula.
:param train: Whether to train model, or only run inference.
:param training_seed: Seed to use for Numpy and Tensorflow random number generation.
:param sampler_manager: SamplerManager object handles samplers for resampling the reset parameters.
:param resampling_interval: Specifies number of simulation steps after which reset parameters are resampled.
"""
self.trainers: Dict[str, Trainer] = {}
self.brain_name_to_identifier: Dict[str, Set] = defaultdict(set)
self.trainer_factory = trainer_factory
self.model_path = model_path
self.summaries_dir = summaries_dir
self.logger = get_logger(__name__)
self.run_id = run_id
self.save_freq = save_freq
self.train_model = train
self.meta_curriculum = meta_curriculum
self.sampler_manager = sampler_manager
self.resampling_interval = resampling_interval
np.random.seed(training_seed)
tf.set_random_seed(training_seed)
def _get_measure_vals(self):
brain_names_to_measure_vals = {}
if self.meta_curriculum:
for (
brain_name,
curriculum,
) in self.meta_curriculum.brains_to_curricula.items():
# Skip brains that are in the metacurriculum but no trainer yet.
if brain_name not in self.trainers:
continue
if curriculum.measure == "progress":
measure_val = self.trainers[brain_name].get_step / float(
self.trainers[brain_name].get_max_steps
)
brain_names_to_measure_vals[brain_name] = measure_val
elif curriculum.measure == "reward":
measure_val = np.mean(self.trainers[brain_name].reward_buffer)
brain_names_to_measure_vals[brain_name] = measure_val
else:
for brain_name, trainer in self.trainers.items():
measure_val = np.mean(trainer.reward_buffer)
brain_names_to_measure_vals[brain_name] = measure_val
return brain_names_to_measure_vals
@timed
def _save_model(self):
"""
Saves current model to checkpoint folder.
"""
for brain_name in self.trainers.keys():
for name_behavior_id in self.brain_name_to_identifier[brain_name]:
self.trainers[brain_name].save_model(name_behavior_id)
self.logger.info("Saved Model")
def _save_model_when_interrupted(self):
self.logger.info(
"Learning was interrupted. Please wait while the graph is generated."
)
self._save_model()
def _export_graph(self):
"""
Exports latest saved models to .nn format for Unity embedding.
"""
for brain_name in self.trainers.keys():
for name_behavior_id in self.brain_name_to_identifier[brain_name]:
self.trainers[brain_name].export_model(name_behavior_id)
@staticmethod
def _create_model_path(model_path):
try:
if not os.path.exists(model_path):
os.makedirs(model_path)
except Exception:
raise UnityEnvironmentException(
"The folder {} containing the "
"generated model could not be "
"accessed. Please make sure the "
"permissions are set correctly.".format(model_path)
)
@timed
def _reset_env(self, env: EnvManager) -> None:
"""Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment.
"""
sampled_reset_param = self.sampler_manager.sample_all()
new_meta_curriculum_config = (
self.meta_curriculum.get_config() if self.meta_curriculum else {}
)
sampled_reset_param.update(new_meta_curriculum_config)
env.reset(config=sampled_reset_param)
def _should_save_model(self, global_step: int) -> bool:
return (
global_step % self.save_freq == 0 and global_step != 0 and self.train_model
)
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
try:
trainer = self.trainers[brain_name]
except KeyError:
trainer = self.trainer_factory.generate(brain_name)
self.trainers[brain_name] = trainer
policy = trainer.create_policy(env_manager.external_brains[name_behavior_id])
trainer.add_policy(parsed_behavior_id, policy)
agent_manager = AgentManager(
policy,
name_behavior_id,
trainer.stats_reporter,
trainer.parameters.get("time_horizon", sys.maxsize),
)
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)
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_model_path(self.model_path)
tf.reset_default_graph()
global_step = 0
last_brain_behavior_ids: Set[str] = set()
try:
# Initial reset
self._reset_env(env_manager)
while self._not_done_training():
external_brain_behavior_ids = set(env_manager.external_brains.keys())
new_behavior_ids = external_brain_behavior_ids - last_brain_behavior_ids
self._create_trainers_and_managers(env_manager, new_behavior_ids)
last_brain_behavior_ids = external_brain_behavior_ids
n_steps = self.advance(env_manager)
for _ in range(n_steps):
global_step += 1
self.reset_env_if_ready(env_manager, global_step)
if self._should_save_model(global_step):
self._save_model()
# Final save Tensorflow model
if global_step != 0 and self.train_model:
self._save_model()
except (
KeyboardInterrupt,
UnityCommunicationException,
UnityEnvironmentException,
) as ex:
if self.train_model:
self._save_model_when_interrupted()
if isinstance(ex, KeyboardInterrupt):
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
if self.train_model:
self._export_graph()
def end_trainer_episodes(
self, env: EnvManager, lessons_incremented: Dict[str, bool]
) -> None:
self._reset_env(env)
# Reward buffers reset takes place only for curriculum learning
# else no reset.
for trainer in self.trainers.values():
trainer.end_episode()
for brain_name, changed in lessons_incremented.items():
if changed:
self.trainers[brain_name].reward_buffer.clear()
def reset_env_if_ready(self, env: EnvManager, steps: int) -> None:
if self.meta_curriculum:
# Get the sizes of the reward buffers.
reward_buff_sizes = {
k: len(t.reward_buffer) for (k, t) in self.trainers.items()
}
# Attempt to increment the lessons of the brains who
# were ready.
lessons_incremented = self.meta_curriculum.increment_lessons(
self._get_measure_vals(), reward_buff_sizes=reward_buff_sizes
)
else:
lessons_incremented = {}
# If any lessons were incremented or the environment is
# ready to be reset
meta_curriculum_reset = any(lessons_incremented.values())
# Check if we are performing generalization training and we have finished the
# specified number of steps for the lesson
generalization_reset = (
not self.sampler_manager.is_empty()
and (steps != 0)
and (self.resampling_interval)
and (steps % self.resampling_interval == 0)
)
if meta_curriculum_reset or generalization_reset:
self.end_trainer_episodes(env, lessons_incremented)
@timed
def advance(self, env: EnvManager) -> int:
# Get steps
with hierarchical_timer("env_step"):
num_steps = env.advance()
# Report current lesson
if self.meta_curriculum:
for brain_name, curr in self.meta_curriculum.brains_to_curricula.items():
if brain_name in self.trainers:
self.trainers[brain_name].stats_reporter.set_stat(
"Environment/Lesson", curr.lesson_num
)
# Advance trainers. This can be done in a separate loop in the future.
with hierarchical_timer("trainer_advance"):
for trainer in self.trainers.values():
trainer.advance()
return num_steps