您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
78 行
3.0 KiB
78 行
3.0 KiB
# # Unity ML-Agents Toolkit
|
|
import logging
|
|
from typing import Dict
|
|
from collections import defaultdict
|
|
|
|
from mlagents.trainers.common.tf_optimizer import TFOptimizer
|
|
from mlagents.trainers.buffer import AgentBuffer
|
|
from mlagents.trainers.trainer import Trainer
|
|
from mlagents.trainers.exception import 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, optimizer: TFOptimizer) -> 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(
|
|
optimizer.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.should_still_train:
|
|
self.clear_update_buffer()
|