text: import os
  import gym
  from tensorboardX import SummaryWriter
  
  from ding.config import compile_config
  from ding.worker import BaseLearner, SampleCollector, BaseSerialEvaluator, NaiveReplayBuffer
  from ding.envs import BaseEnvManager, DingEnvWrapper
  from ding.policy import PPOOffPolicy
  from ding.model import VAC
  from ding.utils import set_pkg_seed, deep_merge_dicts
  from ding.reward_model import RndRewardModel
  from dizoo.classic_control.cartpole.config.cartpole_ppo_rnd_config import cartpole_ppo_rnd_config
  
  
  def wrapped_cartpole_env():
      return DingEnvWrapper(gym.make('CartPole-v0'))
  
  
  def main(cfg, seed=0, max_iterations=int(1e10)):
      cfg = compile_config(
          cfg,
          BaseEnvManager,
          PPOOffPolicy,
          BaseLearner,
          SampleCollector,
          BaseSerialEvaluator,
          NaiveReplayBuffer,
          reward_model=RndRewardModel,
          save_cfg=True
      )
      collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
      collector_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(collector_env_num)], cfg=cfg.env.manager)
      evaluator_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(evaluator_env_num)], cfg=cfg.env.manager)
  
      collector_env.seed(seed)
      evaluator_env.seed(seed, dynamic_seed=False)
      set_pkg_seed(seed, use_cuda=cfg.policy.cuda)
  
      model = VAC(**cfg.policy.model)
      policy = PPOOffPolicy(cfg.policy, model=model)
      tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
      learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
      collector = SampleCollector(
          cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name
      )
      evaluator = BaseSerialEvaluator(
          cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
      )
      replay_buffer = NaiveReplayBuffer(cfg.policy.other.replay_buffer, exp_name=cfg.exp_name)
      reward_model = RndRewardModel(cfg.reward_model, policy.collect_mode.get_attribute('device'), tb_logger)
  
      for _ in range(max_iterations):
          if evaluator.should_eval(learner.train_iter):
              stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
              if stop:
                  break
          new_data = collector.collect(train_iter=learner.train_iter)
          replay_buffer.push(new_data, cur_collector_envstep=collector.envstep)
          reward_model.collect_data(new_data)
          reward_model.train()
          reward_model.clear_data()
          for i in range(cfg.policy.learn.update_per_collect):
              train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter)
              reward_model.estimate(train_data)
              if train_data is not None:
                  learner.train(train_data, collector.envstep)
  
  
  if __name__ == "__main__":
      main(cartpole_ppo_rnd_config)
  