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 (PPO)
# Contains an implementation of PPO as described in: https://arxiv.org/abs/1707.06347
from collections import defaultdict
from typing import cast
import numpy as np
from mlagents_envs.logging_util import get_logger
from mlagents_envs.base_env import BehaviorSpec
from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
from mlagents.trainers.trainer.rl_trainer import RLTrainer
from mlagents.trainers.policy import Policy
from mlagents.trainers.policy.torch_policy import TorchPolicy
from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
from mlagents.trainers.trajectory import Trajectory
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
from mlagents.trainers.settings import TrainerSettings, PPOSettings
logger = get_logger(__name__)
class PPOTrainer(RLTrainer):
"""The PPOTrainer is an implementation of the PPO algorithm."""
def __init__(
self,
behavior_name: str,
reward_buff_cap: int,
trainer_settings: TrainerSettings,
training: bool,
load: bool,
seed: int,
artifact_path: str,
):
"""
Responsible for collecting experiences and training PPO model.
:param behavior_name: The name of the behavior associated with trainer config
:param reward_buff_cap: Max reward history to track in the reward buffer
:param trainer_settings: The parameters for the trainer.
:param training: Whether the trainer is set for training.
:param load: Whether the model should be loaded.
:param seed: The seed the model will be initialized with
:param artifact_path: The directory within which to store artifacts from this trainer.
"""
super().__init__(
behavior_name,
trainer_settings,
training,
load,
artifact_path,
reward_buff_cap,
)
self.hyperparameters: PPOSettings = cast(
PPOSettings, self.trainer_settings.hyperparameters
)
self.seed = seed
self.policy: Policy = None # type: ignore
def _process_trajectory(self, trajectory: Trajectory) -> None:
"""
Takes a trajectory and processes it, putting it into the update buffer.
Processing involves calculating value and advantage targets for model updating step.
:param trajectory: The Trajectory tuple containing the steps to be processed.
"""
super()._process_trajectory(trajectory)
agent_id = trajectory.agent_id # All the agents should have the same ID
agent_buffer_trajectory = trajectory.to_agentbuffer()
# Check if we used group rewards, warn if so.
self._warn_if_group_reward(agent_buffer_trajectory)
# Update the normalization
if self.is_training:
self.policy.update_normalization(agent_buffer_trajectory)
# Get all value estimates
(
value_estimates,
value_next,
value_memories,
) = self.optimizer.get_trajectory_value_estimates(
agent_buffer_trajectory,
trajectory.next_obs,
trajectory.done_reached and not trajectory.interrupted,
)
if value_memories is not None:
agent_buffer_trajectory[BufferKey.CRITIC_MEMORY].set(value_memories)
for name, v in value_estimates.items():
agent_buffer_trajectory[RewardSignalUtil.value_estimates_key(name)].extend(
v
)
self._stats_reporter.add_stat(
f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate",
np.mean(v),
)
# Evaluate all reward functions
self.collected_rewards["environment"][agent_id] += np.sum(
agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]
)
for name, reward_signal in self.optimizer.reward_signals.items():
evaluate_result = (
reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength
)
agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].extend(
evaluate_result
)
# Report the reward signals
self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
# Compute GAE and returns
tmp_advantages = []
tmp_returns = []
for name in self.optimizer.reward_signals:
bootstrap_value = value_next[name]
local_rewards = agent_buffer_trajectory[
RewardSignalUtil.rewards_key(name)
].get_batch()
local_value_estimates = agent_buffer_trajectory[
RewardSignalUtil.value_estimates_key(name)
].get_batch()
local_advantage = get_gae(
rewards=local_rewards,
value_estimates=local_value_estimates,
value_next=bootstrap_value,
gamma=self.optimizer.reward_signals[name].gamma,
lambd=self.hyperparameters.lambd,
)
local_return = local_advantage + local_value_estimates
# This is later use as target for the different value estimates
agent_buffer_trajectory[RewardSignalUtil.returns_key(name)].set(
local_return
)
agent_buffer_trajectory[RewardSignalUtil.advantage_key(name)].set(
local_advantage
)
tmp_advantages.append(local_advantage)
tmp_returns.append(local_return)
# Get global advantages
global_advantages = list(
np.mean(np.array(tmp_advantages, dtype=np.float32), axis=0)
)
global_returns = list(np.mean(np.array(tmp_returns, dtype=np.float32), axis=0))
agent_buffer_trajectory[BufferKey.ADVANTAGES].set(global_advantages)
agent_buffer_trajectory[BufferKey.DISCOUNTED_RETURNS].set(global_returns)
# Append to update buffer
agent_buffer_trajectory.resequence_and_append(
self.update_buffer, training_length=self.policy.sequence_length
)
# If this was a terminal trajectory, append stats and reset reward collection
if trajectory.done_reached:
self._update_end_episode_stats(agent_id, self.optimizer)
def _is_ready_update(self):
"""
Returns whether or not the trainer has enough elements to run update model
:return: A boolean corresponding to whether or not update_model() can be run
"""
size_of_buffer = self.update_buffer.num_experiences
return size_of_buffer > self.hyperparameters.buffer_size
def _update_policy(self):
"""
Uses demonstration_buffer to update the policy.
The reward signal generators must be updated in this method at their own pace.
"""
buffer_length = self.update_buffer.num_experiences
self.cumulative_returns_since_policy_update.clear()
# Make sure batch_size is a multiple of sequence length. During training, we
# will need to reshape the data into a batch_size x sequence_length tensor.
batch_size = (
self.hyperparameters.batch_size
- self.hyperparameters.batch_size % self.policy.sequence_length
)
# Make sure there is at least one sequence
batch_size = max(batch_size, self.policy.sequence_length)
n_sequences = max(
int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
)
advantages = np.array(
self.update_buffer[BufferKey.ADVANTAGES].get_batch(), dtype=np.float32
)
self.update_buffer[BufferKey.ADVANTAGES].set(
(advantages - advantages.mean()) / (advantages.std() + 1e-10)
)
num_epoch = self.hyperparameters.num_epoch
batch_update_stats = defaultdict(list)
for _ in range(num_epoch):
self.update_buffer.shuffle(sequence_length=self.policy.sequence_length)
buffer = self.update_buffer
max_num_batch = buffer_length // batch_size
for i in range(0, max_num_batch * batch_size, batch_size):
update_stats = self.optimizer.update(
buffer.make_mini_batch(i, i + batch_size), n_sequences
)
for stat_name, value in update_stats.items():
batch_update_stats[stat_name].append(value)
for stat, stat_list in batch_update_stats.items():
self._stats_reporter.add_stat(stat, np.mean(stat_list))
if self.optimizer.bc_module:
update_stats = self.optimizer.bc_module.update()
for stat, val in update_stats.items():
self._stats_reporter.add_stat(stat, val)
self._clear_update_buffer()
return True
def create_torch_policy(
self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
) -> TorchPolicy:
"""
Creates a policy with a PyTorch backend and PPO hyperparameters
:param parsed_behavior_id:
:param behavior_spec: specifications for policy construction
:return policy
"""
policy = TorchPolicy(
self.seed,
behavior_spec,
self.trainer_settings,
condition_sigma_on_obs=False, # Faster training for PPO
separate_critic=True, # Match network architecture with TF
)
return policy
def create_ppo_optimizer(self) -> TorchPPOOptimizer:
return TorchPPOOptimizer( # type: ignore
cast(TorchPolicy, self.policy), self.trainer_settings # type: ignore
) # type: ignore
def add_policy(
self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
) -> None:
"""
Adds policy to trainer.
:param parsed_behavior_id: Behavior identifiers that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
if self.policy:
logger.warning(
"Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \
train adversarial games.".format(
self.__class__.__name__
)
)
self.policy = policy
self.policies[parsed_behavior_id.behavior_id] = policy
self.optimizer = self.create_ppo_optimizer()
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
self.model_saver.register(self.policy)
self.model_saver.register(self.optimizer)
self.model_saver.initialize_or_load()
# Needed to resume loads properly
self._step = policy.get_current_step()
def get_policy(self, name_behavior_id: str) -> Policy:
"""
Gets policy from trainer associated with name_behavior_id
:param name_behavior_id: full identifier of policy
"""
return self.policy
def discount_rewards(r, gamma=0.99, value_next=0.0):
"""
Computes discounted sum of future rewards for use in updating value estimate.
:param r: List of rewards.
:param gamma: Discount factor.
:param value_next: T+1 value estimate for returns calculation.
:return: discounted sum of future rewards as list.
"""
discounted_r = np.zeros_like(r)
running_add = value_next
for t in reversed(range(0, r.size)):
running_add = running_add * gamma + r[t]
discounted_r[t] = running_add
return discounted_r
def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95):
"""
Computes generalized advantage estimate for use in updating policy.
:param rewards: list of rewards for time-steps t to T.
:param value_next: Value estimate for time-step T+1.
:param value_estimates: list of value estimates for time-steps t to T.
:param gamma: Discount factor.
:param lambd: GAE weighing factor.
:return: list of advantage estimates for time-steps t to T.
"""
value_estimates = np.append(value_estimates, value_next)
delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1]
advantage = discount_rewards(r=delta_t, gamma=gamma * lambd)
return advantage