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, List, Any, NamedTuple
import numpy as np
from mlagents.envs.brain import BrainInfo
from mlagents.envs.action_info import ActionInfoOutputs
from mlagents.trainers.buffer import Buffer
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 AllRewardsOutput(NamedTuple):
"""
This class stores all of the outputs of the reward signals,
as well as the raw reward from the environment.
"""
reward_signals: RewardSignalResults
environment: np.ndarray
class RLTrainer(Trainer):
"""
This class is the base class for trainers that use Reward Signals.
Contains methods for adding BrainInfos to the Buffer.
"""
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 = {"environment": {}}
self.training_buffer = Buffer()
self.episode_steps = {}
def construct_curr_info(self, next_info: BrainInfo) -> BrainInfo:
"""
Constructs a BrainInfo which contains the most recent previous experiences for all agents
which correspond to the agents in a provided next_info.
:BrainInfo next_info: A t+1 BrainInfo.
:return: curr_info: Reconstructed BrainInfo to match agents of next_info.
"""
visual_observations: List[List[Any]] = [
[] for _ in next_info.visual_observations
] # TODO add types to brain.py methods
vector_observations = []
rewards = []
local_dones = []
max_reacheds = []
agents = []
action_masks = []
for agent_id in next_info.agents:
agent_brain_info = self.training_buffer[agent_id].last_brain_info
if agent_brain_info is None:
agent_brain_info = next_info
agent_index = agent_brain_info.agents.index(agent_id)
for i in range(len(next_info.visual_observations)):
visual_observations[i].append(
agent_brain_info.visual_observations[i][agent_index]
)
vector_observations.append(
agent_brain_info.vector_observations[agent_index]
)
rewards.append(agent_brain_info.rewards[agent_index])
local_dones.append(agent_brain_info.local_done[agent_index])
max_reacheds.append(agent_brain_info.max_reached[agent_index])
agents.append(agent_brain_info.agents[agent_index])
action_masks.append(agent_brain_info.action_masks[agent_index])
curr_info = BrainInfo(
visual_observations,
vector_observations,
rewards,
agents,
local_dones,
max_reacheds,
action_masks,
)
return curr_info
def add_experiences(
self,
curr_info: BrainInfo,
next_info: BrainInfo,
take_action_outputs: ActionInfoOutputs,
) -> None:
"""
Adds experiences to each agent's experience history.
:param curr_info: current BrainInfo.
:param next_info: next BrainInfo.
:param take_action_outputs: The outputs of the Policy's get_action method.
"""
self.trainer_metrics.start_experience_collection_timer()
if take_action_outputs:
self.stats["Policy/Entropy"].append(take_action_outputs["entropy"].mean())
self.stats["Policy/Learning Rate"].append(
take_action_outputs["learning_rate"]
)
for name, signal in self.policy.reward_signals.items():
self.stats[signal.value_name].append(
np.mean(take_action_outputs["value_heads"][name])
)
for agent_id in curr_info.agents:
self.training_buffer[agent_id].last_brain_info = curr_info
self.training_buffer[
agent_id
].last_take_action_outputs = take_action_outputs
if curr_info.agents != next_info.agents:
curr_to_use = self.construct_curr_info(next_info)
else:
curr_to_use = curr_info
# Evaluate and store the reward signals
tmp_reward_signal_outs = {}
for name, signal in self.policy.reward_signals.items():
tmp_reward_signal_outs[name] = signal.evaluate(
curr_to_use, take_action_outputs["action"], next_info
)
# Store the environment reward
tmp_environment = np.array(next_info.rewards)
rewards_out = AllRewardsOutput(
reward_signals=tmp_reward_signal_outs, environment=tmp_environment
)
for agent_id in next_info.agents:
stored_info = self.training_buffer[agent_id].last_brain_info
stored_take_action_outputs = self.training_buffer[
agent_id
].last_take_action_outputs
if stored_info is not None:
idx = stored_info.agents.index(agent_id)
next_idx = next_info.agents.index(agent_id)
if not stored_info.local_done[idx]:
for i, _ in enumerate(stored_info.visual_observations):
self.training_buffer[agent_id]["visual_obs%d" % i].append(
stored_info.visual_observations[i][idx]
)
self.training_buffer[agent_id]["next_visual_obs%d" % i].append(
next_info.visual_observations[i][next_idx]
)
if self.policy.use_vec_obs:
self.training_buffer[agent_id]["vector_obs"].append(
stored_info.vector_observations[idx]
)
self.training_buffer[agent_id]["next_vector_in"].append(
next_info.vector_observations[next_idx]
)
if self.policy.use_recurrent:
self.training_buffer[agent_id]["memory"].append(
self.policy.retrieve_memories([agent_id])[0, :]
)
self.training_buffer[agent_id]["masks"].append(1.0)
self.training_buffer[agent_id]["done"].append(
next_info.local_done[next_idx]
)
# Add the outputs of the last eval
self.add_policy_outputs(stored_take_action_outputs, agent_id, idx)
# Store action masks if necessary
if not self.policy.use_continuous_act:
self.training_buffer[agent_id]["action_mask"].append(
stored_info.action_masks[idx], padding_value=1
)
self.training_buffer[agent_id]["prev_action"].append(
self.policy.retrieve_previous_action([agent_id])[0, :]
)
values = stored_take_action_outputs["value_heads"]
# Add the value outputs if needed
self.add_rewards_outputs(
rewards_out, values, agent_id, idx, next_idx
)
for name, rewards in self.collected_rewards.items():
if agent_id not in rewards:
rewards[agent_id] = 0
if name == "environment":
# Report the reward from the environment
rewards[agent_id] += rewards_out.environment[next_idx]
else:
# Report the reward signals
rewards[agent_id] += rewards_out.reward_signals[
name
].scaled_reward[next_idx]
if not next_info.local_done[next_idx]:
if agent_id not in self.episode_steps:
self.episode_steps[agent_id] = 0
self.episode_steps[agent_id] += 1
self.policy.save_previous_action(
curr_info.agents, take_action_outputs["action"]
)
self.trainer_metrics.end_experience_collection_timer()
def end_episode(self) -> None:
"""
A signal that the Episode has ended. The buffer must be reset.
Get only called when the academy resets.
"""
self.training_buffer.reset_local_buffers()
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 clear_update_buffer(self) -> None:
"""
Clear the buffers that have been built up during inference. If
we're not training, this should be called instead of update_policy.
"""
self.training_buffer.reset_update_buffer()
def add_policy_outputs(
self, take_action_outputs: ActionInfoOutputs, agent_id: str, agent_idx: int
) -> None:
"""
Takes the output of the last action and store it into the training buffer.
We break this out from add_experiences since it is very highly dependent
on the type of trainer.
:param take_action_outputs: The outputs of the Policy's get_action method.
:param agent_id: the Agent we're adding to.
:param agent_idx: the index of the Agent agent_id
"""
raise UnityTrainerException(
"The add_policy_outputs method was not implemented."
)
def add_rewards_outputs(
self,
rewards_out: AllRewardsOutput,
values: Dict[str, np.ndarray],
agent_id: str,
agent_idx: int,
agent_next_idx: int,
) -> None:
"""
Takes the value and evaluated rewards output of the last action and store it
into the training buffer. We break this out from add_experiences since it is very
highly dependent on the type of trainer.
:param take_action_outputs: The outputs of the Policy's get_action method.
:param rewards_dict: Dict of rewards after evaluation
:param agent_id: the Agent we're adding to.
:param agent_idx: the index of the Agent agent_id in the current brain info
:param agent_next_idx: the index of the Agent agent_id in the next brain info
"""
raise UnityTrainerException(
"The add_rewards_outputs method was not implemented."
)