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 (SAC)
# Contains an implementation of SAC as described in https://arxiv.org/abs/1801.01290
# and implemented in https://github.com/hill-a/stable-baselines
import logging
from collections import defaultdict
from typing import Dict
import os
import numpy as np
from mlagents.envs.brain import AllBrainInfo
from mlagents.envs.action_info import ActionInfoOutputs
from mlagents.envs.timers import timed
from mlagents.trainers.sac.policy import SACPolicy
from mlagents.trainers.rl_trainer import RLTrainer, AllRewardsOutput
LOGGER = logging.getLogger("mlagents.trainers")
BUFFER_TRUNCATE_PERCENT = 0.8
class SACTrainer(RLTrainer):
"""
The SACTrainer is an implementation of the SAC algorithm, with support
for discrete actions and recurrent networks.
"""
def __init__(
self, brain, reward_buff_cap, trainer_parameters, training, load, seed, run_id
):
"""
Responsible for collecting experiences and training SAC model.
:param trainer_parameters: The parameters for the trainer (dictionary).
: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 run_id: The The identifier of the current run
"""
super().__init__(brain, trainer_parameters, training, run_id, reward_buff_cap)
self.param_keys = [
"batch_size",
"buffer_size",
"buffer_init_steps",
"hidden_units",
"learning_rate",
"init_entcoef",
"max_steps",
"normalize",
"num_update",
"num_layers",
"time_horizon",
"sequence_length",
"summary_freq",
"tau",
"use_recurrent",
"summary_path",
"memory_size",
"model_path",
"reward_signals",
"vis_encode_type",
]
self.check_param_keys()
self.step = 0
self.train_interval = (
trainer_parameters["train_interval"]
if "train_interval" in trainer_parameters
else 1
)
self.reward_signal_updates_per_train = (
trainer_parameters["reward_signals"]["reward_signal_num_update"]
if "reward_signal_num_update" in trainer_parameters["reward_signals"]
else trainer_parameters["num_update"]
)
self.checkpoint_replay_buffer = (
trainer_parameters["save_replay_buffer"]
if "save_replay_buffer" in trainer_parameters
else False
)
self.policy = SACPolicy(seed, brain, trainer_parameters, self.is_training, load)
# Load the replay buffer if load
if load and self.checkpoint_replay_buffer:
try:
self.load_replay_buffer()
except (AttributeError, FileNotFoundError):
LOGGER.warning(
"Replay buffer was unable to load, starting from scratch."
)
LOGGER.debug(
"Loaded update buffer with {} sequences".format(
len(self.training_buffer.update_buffer["actions"])
)
)
for _reward_signal in self.policy.reward_signals.keys():
self.collected_rewards[_reward_signal] = {}
self.episode_steps = {}
def save_model(self) -> None:
"""
Saves the model. Overrides the default save_model since we want to save
the replay buffer as well.
"""
self.policy.save_model(self.get_step)
if self.checkpoint_replay_buffer:
self.save_replay_buffer()
def save_replay_buffer(self) -> None:
"""
Save the training buffer's update buffer to a pickle file.
"""
filename = os.path.join(self.policy.model_path, "last_replay_buffer.hdf5")
LOGGER.info("Saving Experience Replay Buffer to {}".format(filename))
with open(filename, "wb") as file_object:
self.training_buffer.update_buffer.save_to_file(file_object)
def load_replay_buffer(self) -> None:
"""
Loads the last saved replay buffer from a file.
"""
filename = os.path.join(self.policy.model_path, "last_replay_buffer.hdf5")
LOGGER.info("Loading Experience Replay Buffer from {}".format(filename))
with open(filename, "rb+") as file_object:
self.training_buffer.update_buffer.load_from_file(file_object)
LOGGER.info(
"Experience replay buffer has {} experiences.".format(
len(self.training_buffer.update_buffer["actions"])
)
)
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.
"""
actions = take_action_outputs["action"]
self.training_buffer[agent_id]["actions"].append(actions[agent_idx])
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 output of the last action and store it into the training buffer.
"""
self.training_buffer[agent_id]["environment_rewards"].append(
rewards_out.environment[agent_next_idx]
)
def process_experiences(
self, current_info: AllBrainInfo, next_info: AllBrainInfo
) -> None:
"""
Checks agent histories for processing condition, and processes them as necessary.
:param current_info: Dictionary of all current brains and corresponding BrainInfo.
:param next_info: Dictionary of all next brains and corresponding BrainInfo.
"""
info = next_info[self.brain_name]
if self.is_training:
self.policy.update_normalization(info.vector_observations)
for l in range(len(info.agents)):
agent_actions = self.training_buffer[info.agents[l]]["actions"]
if (
info.local_done[l]
or len(agent_actions) >= self.trainer_parameters["time_horizon"]
) and len(agent_actions) > 0:
agent_id = info.agents[l]
# Bootstrap using last brain info. Set last element to duplicate obs and remove dones.
if info.max_reached[l]:
bootstrapping_info = self.training_buffer[agent_id].last_brain_info
idx = bootstrapping_info.agents.index(agent_id)
for i, obs in enumerate(bootstrapping_info.visual_observations):
self.training_buffer[agent_id]["next_visual_obs%d" % i][
-1
] = obs[idx]
if self.policy.use_vec_obs:
self.training_buffer[agent_id]["next_vector_in"][
-1
] = bootstrapping_info.vector_observations[idx]
self.training_buffer[agent_id]["done"][-1] = False
self.training_buffer.append_update_buffer(
agent_id,
batch_size=None,
training_length=self.policy.sequence_length,
)
self.training_buffer[agent_id].reset_agent()
if info.local_done[l]:
self.stats["Environment/Episode Length"].append(
self.episode_steps.get(agent_id, 0)
)
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.stats["Environment/Cumulative Reward"].append(
rewards.get(agent_id, 0)
)
self.reward_buffer.appendleft(rewards.get(agent_id, 0))
rewards[agent_id] = 0
else:
self.stats[
self.policy.reward_signals[name].stat_name
].append(rewards.get(agent_id, 0))
rewards[agent_id] = 0
def is_ready_update(self) -> bool:
"""
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
"""
return (
len(self.training_buffer.update_buffer["actions"])
>= self.trainer_parameters["batch_size"]
and self.step >= self.trainer_parameters["buffer_init_steps"]
)
@timed
def update_policy(self) -> None:
"""
If train_interval is met, update the SAC policy given the current reward signals.
If reward_signal_train_interval is met, update the reward signals from the buffer.
"""
if self.step % self.train_interval == 0:
self.trainer_metrics.start_policy_update_timer(
number_experiences=len(self.training_buffer.update_buffer["actions"]),
mean_return=float(np.mean(self.cumulative_returns_since_policy_update)),
)
self.update_sac_policy()
self.update_reward_signals()
self.trainer_metrics.end_policy_update()
def update_sac_policy(self) -> None:
"""
Uses demonstration_buffer to update the policy.
The reward signal generators are updated using different mini batches.
If we want to imitate http://arxiv.org/abs/1809.02925 and similar papers, where the policy is updated
N times, then the reward signals are updated N times, then reward_signal_updates_per_train
is greater than 1 and the reward signals are not updated in parallel.
"""
self.cumulative_returns_since_policy_update.clear()
n_sequences = max(
int(self.trainer_parameters["batch_size"] / self.policy.sequence_length), 1
)
num_updates = self.trainer_parameters["num_update"]
batch_update_stats: Dict[str, list] = defaultdict(list)
for _ in range(num_updates):
LOGGER.debug("Updating SAC policy at step {}".format(self.step))
buffer = self.training_buffer.update_buffer
if (
len(self.training_buffer.update_buffer["actions"])
>= self.trainer_parameters["batch_size"]
):
sampled_minibatch = buffer.sample_mini_batch(
self.trainer_parameters["batch_size"],
sequence_length=self.policy.sequence_length,
)
# Get rewards for each reward
for name, signal in self.policy.reward_signals.items():
sampled_minibatch[
"{}_rewards".format(name)
] = signal.evaluate_batch(sampled_minibatch).scaled_reward
update_stats = self.policy.update(sampled_minibatch, n_sequences)
for stat_name, value in update_stats.items():
batch_update_stats[stat_name].append(value)
# Truncate update buffer if neccessary. Truncate more than we need to to avoid truncating
# a large buffer at each update.
if (
len(self.training_buffer.update_buffer["actions"])
> self.trainer_parameters["buffer_size"]
):
self.training_buffer.truncate_update_buffer(
int(self.trainer_parameters["buffer_size"] * BUFFER_TRUNCATE_PERCENT)
)
for stat, stat_list in batch_update_stats.items():
self.stats[stat].append(np.mean(stat_list))
if self.policy.bc_module:
update_stats = self.policy.bc_module.update()
for stat, val in update_stats.items():
self.stats[stat].append(val)
def update_reward_signals(self) -> None:
"""
Iterate through the reward signals and update them. Unlike in PPO,
do it separate from the policy so that it can be done at a different
interval.
This function should only be used to simulate
http://arxiv.org/abs/1809.02925 and similar papers, where the policy is updated
N times, then the reward signals are updated N times. Normally, the reward signal
and policy are updated in parallel.
"""
buffer = self.training_buffer.update_buffer
num_updates = self.reward_signal_updates_per_train
n_sequences = max(
int(self.trainer_parameters["batch_size"] / self.policy.sequence_length), 1
)
batch_update_stats: Dict[str, list] = defaultdict(list)
for _ in range(num_updates):
# Get minibatches for reward signal update if needed
reward_signal_minibatches = {}
for name, signal in self.policy.reward_signals.items():
LOGGER.debug("Updating {} at step {}".format(name, self.step))
# Some signals don't need a minibatch to be sampled - so we don't!
if signal.update_dict:
reward_signal_minibatches[name] = buffer.sample_mini_batch(
self.trainer_parameters["batch_size"],
sequence_length=self.policy.sequence_length,
)
update_stats = self.policy.update_reward_signals(
reward_signal_minibatches, 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[stat].append(np.mean(stat_list))