Source code for lzero.entry.eval_alphazero

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 lzero.worker import AlphaZeroEvaluator


[docs]def eval_alphazero( 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 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. - 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')) 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, ) while True: # ============================================================== # eval trained model # ============================================================== returns = [] for i in range(num_episodes_each_seed): stop_flag, episode_info = evaluator.eval() 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.simulation_env_id in ['tictactoe', 'connect4', 'gomoku', 'chess']: 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