import gym from ditk import logging from ding.model import DQN from ding.policy import DQNPolicy from ding.reward_model import RndRewardModel from ding.envs import DingEnvWrapper, BaseEnvManagerV2 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, trainer, \ eps_greedy_handler, CkptSaver from ding.utils import set_pkg_seed from dizoo.classic_control.cartpole.config.cartpole_dqn_rnd_config import main_config, create_config def main(): logging.getLogger().setLevel(logging.INFO) cfg = compile_config(main_config, create_cfg=create_config, auto=True) with task.start(async_mode=False, ctx=OnlineRLContext()): collector_env = BaseEnvManagerV2( env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager ) evaluator_env = BaseEnvManagerV2( env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.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) reward_model = RndRewardModel(cfg.reward_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(trainer(cfg, reward_model)) task.use(data_pusher(cfg, buffer_)) task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_, reward_model=reward_model)) task.use(CkptSaver(policy, cfg.exp_name, train_freq=100)) task.run() if __name__ == "__main__": main()