import logging
import os
from functools import partial
from typing import Optional, Tuple
import torch
from ding.config import compile_config
from ding.envs import create_env_manager
from ding.envs import get_vec_env_setting
from ding.policy import create_policy
from ding.utils import set_pkg_seed
from ding.worker import BaseLearner, create_buffer
from tensorboardX import SummaryWriter
from lzero.policy import visit_count_temperature
from lzero.worker import AlphaZeroCollector, AlphaZeroEvaluator
[docs]def train_alphazero(
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 AlphaZero.
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.
- env_setting (:obj:`Optional[List[Any]]`): A list with 3 elements: \
``BaseEnv`` subclass, collector env config, and evaluator env config.
- 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
create_cfg.policy.type = create_cfg.policy.type
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
env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg])
evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])
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)
replay_buffer = create_buffer(cfg.policy.other.replay_buffer, tb_logger=tb_logger, exp_name=cfg.exp_name)
policy_config = cfg.policy
batch_size = policy_config.batch_size
collector = AlphaZeroCollector(
env=collector_env,
policy=policy.collect_mode,
tb_logger=tb_logger,
exp_name=cfg.exp_name,
)
evaluator = AlphaZeroEvaluator(
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,
)
# ==============================================================
# 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
)
# Evaluate policy performance
if evaluator.should_eval(learner.train_iter) and learner.train_iter > 0:
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)
new_data = sum(new_data, [])
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.
collected_transitions_num = len(new_data)
update_per_collect = int(collected_transitions_num * cfg.policy.replay_ratio)
replay_buffer.push(new_data, cur_collector_envstep=collector.envstep)
# Learn policy from collected data
for i in range(update_per_collect):
# Learner will train ``update_per_collect`` times in one iteration.
train_data = replay_buffer.sample(batch_size, learner.train_iter)
if train_data is None:
logging.warning(
'The data in replay_buffer is not sufficient to sample a mini-batch.'
'continue to collect now ....'
)
break
learner.train(train_data, collector.envstep)
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