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
from typing import List, Deque
import abc
from collections import deque
from mlagents_envs.logging_util import get_logger
from mlagents_envs.timers import timed
from mlagents_envs.base_env import BehaviorSpec
from mlagents.model_serialization import export_policy_model, SerializationSettings
from mlagents.trainers.policy.tf_policy import TFPolicy
from mlagents.trainers.stats import StatsReporter
from mlagents.trainers.trajectory import Trajectory
from mlagents.trainers.agent_processor import AgentManagerQueue
from mlagents.trainers.policy import Policy
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
from mlagents.trainers.settings import TrainerSettings
logger = get_logger(__name__)
class Trainer(abc.ABC):
"""This class is the base class for the mlagents_envs.trainers"""
def __init__(
self,
brain_name: str,
trainer_settings: TrainerSettings,
training: bool,
artifact_path: str,
reward_buff_cap: int = 1,
):
"""
Responsible for collecting experiences and training a neural network model.
:param brain_name: Brain name of brain to be trained.
:param trainer_settings: The parameters for the trainer (dictionary).
:param training: Whether the trainer is set for training.
:param artifact_path: The directory within which to store artifacts from this trainer
:param reward_buff_cap:
"""
self.brain_name = brain_name
self.trainer_settings = trainer_settings
self._threaded = trainer_settings.threaded
self._stats_reporter = StatsReporter(brain_name)
self.is_training = training
self._reward_buffer: Deque[float] = deque(maxlen=reward_buff_cap)
self.policy_queues: List[AgentManagerQueue[Policy]] = []
self.trajectory_queues: List[AgentManagerQueue[Trajectory]] = []
self.step: int = 0
self.artifact_path = artifact_path
self.summary_freq = self.trainer_settings.summary_freq
@property
def stats_reporter(self):
"""
Returns the stats reporter associated with this Trainer.
"""
return self._stats_reporter
@property
def parameters(self) -> TrainerSettings:
"""
Returns the trainer parameters of the trainer.
"""
return self.trainer_settings
@property
def get_max_steps(self) -> int:
"""
Returns the maximum number of steps. Is used to know when the trainer should be stopped.
:return: The maximum number of steps of the trainer
"""
return self.trainer_settings.max_steps
@property
def get_step(self) -> int:
"""
Returns the number of steps the trainer has performed
:return: the step count of the trainer
"""
return self.step
@property
def threaded(self) -> bool:
"""
Whether or not to run the trainer in a thread. True allows the trainer to
update the policy while the environment is taking steps. Set to False to
enforce strict on-policy updates (i.e. don't update the policy when taking steps.)
"""
return self._threaded
@property
def should_still_train(self) -> bool:
"""
Returns whether or not the trainer should train. A Trainer could
stop training if it wasn't training to begin with, or if max_steps
is reached.
"""
return self.is_training and self.get_step <= self.get_max_steps
@property
def reward_buffer(self) -> Deque[float]:
"""
Returns the reward buffer. The reward buffer contains the cumulative
rewards of the most recent episodes completed by agents using this
trainer.
:return: the reward buffer.
"""
return self._reward_buffer
@timed
def save_model(self, name_behavior_id: str) -> None:
"""
Saves the model
"""
self.get_policy(name_behavior_id).save_model(self.get_step)
def export_model(self, name_behavior_id: str) -> None:
"""
Exports the model
"""
policy = self.get_policy(name_behavior_id)
settings = SerializationSettings(policy.model_path, self.brain_name)
export_policy_model(settings, policy.graph, policy.sess)
@abc.abstractmethod
def end_episode(self):
"""
A signal that the Episode has ended. The buffer must be reset.
Get only called when the academy resets.
"""
pass
@abc.abstractmethod
def create_policy(
self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
) -> TFPolicy:
"""
Creates policy
"""
pass
@abc.abstractmethod
def add_policy(
self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy
) -> None:
"""
Adds policy to trainer.
"""
pass
@abc.abstractmethod
def get_policy(self, name_behavior_id: str) -> TFPolicy:
"""
Gets policy from trainer.
"""
pass
@abc.abstractmethod
def advance(self) -> None:
"""
Advances the trainer. Typically, this means grabbing trajectories
from all subscribed trajectory queues (self.trajectory_queues), and updating
a policy using the steps in them, and if needed pushing a new policy onto the right
policy queues (self.policy_queues).
"""
pass
def publish_policy_queue(self, policy_queue: AgentManagerQueue[Policy]) -> None:
"""
Adds a policy queue to the list of queues to publish to when this Trainer
makes a policy update
:param policy_queue: Policy queue to publish to.
"""
self.policy_queues.append(policy_queue)
def subscribe_trajectory_queue(
self, trajectory_queue: AgentManagerQueue[Trajectory]
) -> None:
"""
Adds a trajectory queue to the list of queues for the trainer to ingest Trajectories from.
:param trajectory_queue: Trajectory queue to read from.
"""
self.trajectory_queues.append(trajectory_queue)