import os import gym import torch from tensorboardX import SummaryWriter from easydict import EasyDict from functools import partial from ding.config import compile_config from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer from ding.envs import BaseEnvManager, DingEnvWrapper from ding.envs import get_vec_env_setting, create_env_manager from ding.policy import DQNPolicy from ding.model import DQN from ding.utils import set_pkg_seed from ding.rl_utils import get_epsilon_greedy_fn from dizoo.atari.config.serial.spaceinvaders.spaceinvaders_dqn_config import main_config, create_config def main(rl_cfg, seed=0): main_cfg, create_cfg = rl_cfg cfg = compile_config( main_cfg, BaseEnvManager, DQNPolicy, BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer, create_cfg=create_cfg, save_cfg=True ) create_cfg.policy.type = create_cfg.policy.type + '_command' env_fn = None cfg = compile_config(cfg, seed=seed, env=env_fn, 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.enable_save_replay(cfg.env.replay_path) # switch save replay interface # Set random seed for all package and instance evaluator_env.seed(seed, dynamic_seed=False) set_pkg_seed(seed, use_cuda=cfg.policy.cuda) # Set up RL Policy model = DQN(**cfg.policy.model) policy = DQNPolicy(cfg.policy, model=model) policy.eval_mode.load_state_dict(torch.load(cfg.policy.load_path, map_location='cpu')) # evaluate tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) evaluator = InteractionSerialEvaluator( cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name ) evaluator.eval() if __name__ == "__main__": main(rl_cfg=(main_config, create_config), seed=0)