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| import gym | |
| from ditk import logging | |
| from ding.model import DQN | |
| from ding.policy import DQNPolicy | |
| from ding.envs import DingEnvWrapper, SubprocessEnvManagerV2 | |
| from ding.envs.env_wrappers import MaxAndSkipWrapper, WarpFrameWrapper, ScaledFloatFrameWrapper, FrameStackWrapper, \ | |
| EvalEpisodeReturnWrapper, TimeLimitWrapper | |
| from ding.data import DequeBuffer | |
| from ding.config import compile_config | |
| from ding.framework import task | |
| from ding.framework.context import OnlineRLContext | |
| from ding.framework.middleware import OffPolicyLearner, StepCollector, interaction_evaluator, data_pusher, \ | |
| eps_greedy_handler, CkptSaver, nstep_reward_enhancer | |
| from ding.utils import set_pkg_seed | |
| from mario_dqn_config import main_config, create_config | |
| import gym_super_mario_bros | |
| from nes_py.wrappers import JoypadSpace | |
| def wrapped_mario_env(): | |
| return DingEnvWrapper( | |
| JoypadSpace(gym_super_mario_bros.make("SuperMarioBros-1-1-v0"), [["right"], ["right", "A"]]), | |
| cfg={ | |
| 'env_wrapper': [ | |
| lambda env: MaxAndSkipWrapper(env, skip=4), | |
| lambda env: WarpFrameWrapper(env, size=84), | |
| lambda env: ScaledFloatFrameWrapper(env), | |
| lambda env: FrameStackWrapper(env, n_frames=4), | |
| lambda env: TimeLimitWrapper(env, max_limit=400), | |
| lambda env: EvalEpisodeReturnWrapper(env), | |
| ] | |
| } | |
| ) | |
| def main(): | |
| filename = '{}/log.txt'.format(main_config.exp_name) | |
| logging.getLogger(with_files=[filename]).setLevel(logging.INFO) | |
| cfg = compile_config(main_config, create_cfg=create_config, auto=True) | |
| with task.start(async_mode=False, ctx=OnlineRLContext()): | |
| collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num | |
| collector_env = SubprocessEnvManagerV2( | |
| env_fn=[wrapped_mario_env for _ in range(collector_env_num)], cfg=cfg.env.manager | |
| ) | |
| evaluator_env = SubprocessEnvManagerV2( | |
| env_fn=[wrapped_mario_env for _ in range(evaluator_env_num)], cfg=cfg.env.manager | |
| ) | |
| set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) | |
| model = DQN(**cfg.policy.model) | |
| buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size) | |
| policy = DQNPolicy(cfg.policy, model=model) | |
| task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env)) | |
| task.use(eps_greedy_handler(cfg)) | |
| task.use(StepCollector(cfg, policy.collect_mode, collector_env)) | |
| task.use(nstep_reward_enhancer(cfg)) | |
| task.use(data_pusher(cfg, buffer_)) | |
| task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_)) | |
| task.use(CkptSaver(policy, cfg.exp_name, train_freq=1000)) | |
| task.run() | |
| if __name__ == "__main__": | |
| main() | |