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import os
from functools import partial
from typing import Optional, Tuple
import numpy as np
import torch
from tensorboardX import SummaryWriter
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
from lzero.worker import MuZeroEvaluator
def eval_muzero(
input_cfg: Tuple[dict, dict],
seed: int = 0,
model: Optional[torch.nn.Module] = None,
model_path: Optional[str] = None,
num_episodes_each_seed: int = 1,
print_seed_details: int = False,
) -> 'Policy': # noqa
"""
Overview:
The eval entry for MCTS+RL algorithms, including MuZero, EfficientZero, Sampled EfficientZero.
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``.
Returns:
- policy (:obj:`Policy`): Converged policy.
"""
cfg, create_cfg = input_cfg
assert create_cfg.policy.type in ['efficientzero', 'muzero', 'stochastic_muzero', 'gumbel_muzero', 'sampled_efficientzero'], \
"LightZero now only support the following algo.: 'efficientzero', 'muzero', 'stochastic_muzero', 'gumbel_muzero', 'sampled_efficientzero'"
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)
evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])
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
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')
while True:
# ==============================================================
# eval trained model
# ==============================================================
returns = []
for i in range(num_episodes_each_seed):
stop_flag, episode_info = evaluator.eval(learner.save_checkpoint, learner.train_iter)
returns.append(episode_info['eval_episode_return'])
returns = np.array(returns)
if print_seed_details:
print("=" * 20)
print(f'In seed {seed}, returns: {returns}')
if cfg.policy.env_type == 'board_games':
print(
f'win rate: {len(np.where(returns == 1.)[0]) / num_episodes_each_seed}, draw rate: {len(np.where(returns == 0.)[0]) / num_episodes_each_seed}, lose rate: {len(np.where(returns == -1.)[0]) / num_episodes_each_seed}'
)
print("=" * 20)
return returns.mean(), returns
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