import logging
import os
from typing import Optional
from typing import Tuple
import torch
from tensorboardX import SummaryWriter
from ding.config import compile_config
from ding.envs import DingEnvWrapper, BaseEnvManager
from ding.policy import create_policy
from ding.rl_utils import get_epsilon_greedy_fn
from ding.utils import set_pkg_seed
from ding.worker import BaseLearner
from lzero.envs.get_wrapped_env import get_wrappered_env
from lzero.policy import visit_count_temperature
from lzero.worker import MuZeroCollector, MuZeroEvaluator
[docs]def train_muzero_with_gym_env(
input_cfg: Tuple[dict, dict],
seed: int = 0,
model: Optional[torch.nn.Module] = None,
model_path: Optional[str] = None,
max_train_iter: Optional[int] = int(1e10),
max_env_step: Optional[int] = int(1e10),
) -> 'Policy': # noqa
"""
Overview:
The train entry for MCTS+RL algorithms, including MuZero, EfficientZero, Sampled EfficientZero.
We create a gym environment using env_id parameter, and then convert it to the format required by LightZero using LightZeroEnvWrapper class.
Please refer to the get_wrappered_env method for more details.
Arguments:
- input_cfg (:obj:`Tuple[dict, dict]`): Config in dict type.
``Tuple[dict, dict]`` type means [user_config, create_cfg].
- seed (:obj:`int`): Random seed.
- model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module.
- model_path (:obj:`Optional[str]`): The pretrained model path, which should
point to the ckpt file of the pretrained model, and an absolute path is recommended.
In LightZero, the path is usually something like ``exp_name/ckpt/ckpt_best.pth.tar``.
- max_train_iter (:obj:`Optional[int]`): Maximum policy update iterations in training.
- max_env_step (:obj:`Optional[int]`): Maximum collected environment interaction steps.
Returns:
- policy (:obj:`Policy`): Converged policy.
"""
cfg, create_cfg = input_cfg
assert create_cfg.policy.type in ['efficientzero', 'muzero', 'sampled_efficientzero'], \
"train_muzero entry now only support the following algo.: 'efficientzero', 'muzero', 'sampled_efficientzero'"
if create_cfg.policy.type == 'muzero':
from lzero.mcts import MuZeroGameBuffer as GameBuffer
elif create_cfg.policy.type == 'efficientzero':
from lzero.mcts import EfficientZeroGameBuffer as GameBuffer
elif create_cfg.policy.type == 'sampled_efficientzero':
from lzero.mcts import SampledEfficientZeroGameBuffer as GameBuffer
if cfg.policy.cuda and torch.cuda.is_available():
cfg.policy.device = 'cuda'
else:
cfg.policy.device = 'cpu'
cfg = compile_config(cfg, seed=seed, env=None, auto=True, create_cfg=create_cfg, save_cfg=True)
# Create main components: env, policy
collector_env_cfg = DingEnvWrapper.create_collector_env_cfg(cfg.env)
evaluator_env_cfg = DingEnvWrapper.create_evaluator_env_cfg(cfg.env)
collector_env = BaseEnvManager(
[get_wrappered_env(c, cfg.env.env_id) for c in collector_env_cfg], cfg=BaseEnvManager.default_config()
)
evaluator_env = BaseEnvManager(
[get_wrappered_env(c, cfg.env.env_id) for c in evaluator_env_cfg], cfg=BaseEnvManager.default_config()
)
collector_env.seed(cfg.seed)
evaluator_env.seed(cfg.seed, dynamic_seed=False)
set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval'])
# load pretrained model
if model_path is not None:
policy.learn_mode.load_state_dict(torch.load(model_path, map_location=cfg.policy.device))
# Create worker components: learner, collector, evaluator, replay buffer, commander.
tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
# ==============================================================
# MCTS+RL algorithms related core code
# ==============================================================
policy_config = cfg.policy
batch_size = policy_config.batch_size
# specific game buffer for MCTS+RL algorithms
replay_buffer = GameBuffer(policy_config)
collector = MuZeroCollector(
env=collector_env,
policy=policy.collect_mode,
tb_logger=tb_logger,
exp_name=cfg.exp_name,
policy_config=policy_config
)
evaluator = MuZeroEvaluator(
eval_freq=cfg.policy.eval_freq,
n_evaluator_episode=cfg.env.n_evaluator_episode,
stop_value=cfg.env.stop_value,
env=evaluator_env,
policy=policy.eval_mode,
tb_logger=tb_logger,
exp_name=cfg.exp_name,
policy_config=policy_config
)
# ==============================================================
# Main loop
# ==============================================================
# Learner's before_run hook.
learner.call_hook('before_run')
if cfg.policy.update_per_collect is not None:
update_per_collect = cfg.policy.update_per_collect
while True:
collect_kwargs = {}
# set temperature for visit count distributions according to the train_iter,
# please refer to Appendix D in MuZero paper for details.
collect_kwargs['temperature'] = visit_count_temperature(
policy_config.manual_temperature_decay,
policy_config.fixed_temperature_value,
policy_config.threshold_training_steps_for_final_temperature,
trained_steps=learner.train_iter
)
if policy_config.eps.eps_greedy_exploration_in_collect:
epsilon_greedy_fn = get_epsilon_greedy_fn(
start=policy_config.eps.start,
end=policy_config.eps.end,
decay=policy_config.eps.decay,
type_=policy_config.eps.type
)
collect_kwargs['epsilon'] = epsilon_greedy_fn(collector.envstep)
else:
collect_kwargs['epsilon'] = 0.0
# Evaluate policy performance.
if evaluator.should_eval(learner.train_iter):
stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
if stop:
break
# Collect data by default config n_sample/n_episode.
new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs)
if cfg.policy.update_per_collect is None:
# update_per_collect is None, then update_per_collect is set to the number of collected transitions multiplied by the replay_ratio.
# The length of game_segment (i.e., len(game_segment.action_segment)) can be smaller than cfg.policy.game_segment_length if it represents the final segment of the game.
# On the other hand, its length will be less than cfg.policy.game_segment_length + padding_length when it is not the last game segment. Typically, padding_length is the sum of unroll_steps and td_steps.
collected_transitions_num = sum(min(len(game_segment), cfg.policy.game_segment_length) for game_segment in new_data[0])
update_per_collect = int(collected_transitions_num * cfg.policy.replay_ratio)
# save returned new_data collected by the collector
replay_buffer.push_game_segments(new_data)
# remove the oldest data if the replay buffer is full.
replay_buffer.remove_oldest_data_to_fit()
# Learn policy from collected data.
for i in range(update_per_collect):
# Learner will train ``update_per_collect`` times in one iteration.
if replay_buffer.get_num_of_transitions() > batch_size:
train_data = replay_buffer.sample(batch_size, policy)
else:
logging.warning(
f'The data in replay_buffer is not sufficient to sample a mini-batch: '
f'batch_size: {batch_size}, '
f'{replay_buffer} '
f'continue to collect now ....'
)
break
# The core train steps for MCTS+RL algorithms.
log_vars = learner.train(train_data, collector.envstep)
if cfg.policy.use_priority:
replay_buffer.update_priority(train_data, log_vars[0]['value_priority_orig'])
if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
break
# Learner's after_run hook.
learner.call_hook('after_run')
return policy