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
import logging
from typing import Dict
from collections import defaultdict
from mlagents.trainers.tf_policy import TFPolicy
from mlagents.trainers.buffer import AgentBuffer
from mlagents.trainers.trainer import Trainer, UnityTrainerException
from mlagents.trainers.components.reward_signals import RewardSignalResult
LOGGER = logging.getLogger("mlagents.trainers")
RewardSignalResults = Dict[str, RewardSignalResult]
class RLTrainer(Trainer): # pylint: disable=abstract-method
"""
This class is the base class for trainers that use Reward Signals.
"""
def __init__(self, *args, **kwargs):
super(RLTrainer, self).__init__(*args, **kwargs)
# Make sure we have at least one reward_signal
if not self.trainer_parameters["reward_signals"]:
raise UnityTrainerException(
"No reward signals were defined. At least one must be used with {}.".format(
self.__class__.__name__
)
)
# collected_rewards is a dictionary from name of reward signal to a dictionary of agent_id to cumulative reward
# used for reporting only. We always want to report the environment reward to Tensorboard, regardless
# of what reward signals are actually present.
self.collected_rewards: Dict[str, Dict[str, int]] = {
"environment": defaultdict(lambda: 0)
}
self.update_buffer: AgentBuffer = AgentBuffer()
self.episode_steps: Dict[str, int] = defaultdict(lambda: 0)
def end_episode(self) -> None:
"""
A signal that the Episode has ended. The buffer must be reset.
Get only called when the academy resets.
"""
for agent_id in self.episode_steps:
self.episode_steps[agent_id] = 0
for rewards in self.collected_rewards.values():
for agent_id in rewards:
rewards[agent_id] = 0
def _update_end_episode_stats(self, agent_id: str, policy: TFPolicy) -> None:
self.episode_steps[agent_id] = 0
for name, rewards in self.collected_rewards.items():
if name == "environment":
self.cumulative_returns_since_policy_update.append(
rewards.get(agent_id, 0)
)
self.reward_buffer.appendleft(rewards.get(agent_id, 0))
rewards[agent_id] = 0
else:
self.stats_reporter.add_stat(
policy.reward_signals[name].stat_name, rewards.get(agent_id, 0)
)
rewards[agent_id] = 0
def clear_update_buffer(self) -> None:
"""
Clear the buffers that have been built up during inference.
"""
self.update_buffer.reset_agent()
def advance(self) -> None:
"""
Steps the trainer, taking in trajectories and updates if ready
"""
super().advance()
if not self.is_training:
self.clear_update_buffer()