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import os |
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import gym |
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import torch |
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from tensorboardX import SummaryWriter |
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from easydict import EasyDict |
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from ding.config import compile_config |
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from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer |
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from ding.envs import BaseEnvManager, DingEnvWrapper |
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from ding.policy import DQNPolicy |
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from ding.model import DQN |
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from ding.utils import set_pkg_seed |
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from ding.rl_utils import get_epsilon_greedy_fn |
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from dizoo.classic_control.cartpole.config.cartpole_dqn_config import cartpole_dqn_config |
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def wrapped_cartpole_env(): |
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return DingEnvWrapper( |
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gym.make('CartPole-v0'), |
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EasyDict(env_wrapper='default'), |
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) |
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def main(cfg, seed=0): |
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cfg = compile_config( |
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cfg, |
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BaseEnvManager, |
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DQNPolicy, |
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BaseLearner, |
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SampleSerialCollector, |
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InteractionSerialEvaluator, |
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AdvancedReplayBuffer, |
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save_cfg=True |
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) |
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evaluator_env_num = cfg.env.evaluator_env_num |
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evaluator_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(evaluator_env_num)], cfg=cfg.env.manager) |
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evaluator_env.enable_save_replay(cfg.env.replay_path) |
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evaluator_env.seed(seed, dynamic_seed=False) |
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set_pkg_seed(seed, use_cuda=cfg.policy.cuda) |
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model = DQN(**cfg.policy.model) |
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policy = DQNPolicy(cfg.policy, model=model) |
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policy.eval_mode.load_state_dict(torch.load(cfg.policy.load_path, map_location='cpu')) |
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tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) |
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evaluator = InteractionSerialEvaluator( |
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cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name |
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) |
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evaluator.eval() |
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if __name__ == "__main__": |
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main(cartpole_dqn_config) |
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