Source code for lzero.entry.eval_muzero_with_gym_env

import os
from typing import Optional
from typing import Tuple

import numpy as np
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.utils import set_pkg_seed
from ding.worker import BaseLearner
from lzero.envs.get_wrapped_env import get_wrappered_env
from lzero.worker import MuZeroEvaluator


[docs]def eval_muzero_with_gym_env( 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. 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``. Returns: - policy (:obj:`Policy`): Converged policy. """ cfg, create_cfg = input_cfg assert create_cfg.policy.type in ['efficientzero', 'muzero', 'sampled_efficientzero'], \ "LightZero noow only support the following algo.: 'efficientzero', '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 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 # specific game buffer for MCTS+RL algorithms 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, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter) returns.append(reward) 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