code: def main():
      env = gym.make(args.env)
      env.seed(ENV_SEED)
      env = ActionMappingWrapper(env)
  
      obs_dim = env.observation_space.shape[0]
      act_dim = env.action_space.shape[0]
  
      model = MujocoModel(act_dim)
      algorithm = parl.algorithms.DDPG(
          model,
          gamma=GAMMA,
          tau=TAU,
          actor_lr=ACTOR_LR,
          critic_lr=CRITIC_LR,
      )
      agent = MujocoAgent(algorithm, obs_dim, act_dim)
  
      rpm = ReplayMemory(MEMORY_SIZE, obs_dim, act_dim)
  
      while rpm.size() < MEMORY_WARMUP_SIZE:
          run_train_episode(env, agent, rpm)
  
      episode = 0
      while episode < args.train_total_episode:
          for i in range(50):
              train_reward = run_train_episode(env, agent, rpm)
              episode += 1
              logger.info(
                  "Episode: {} Reward: {}".format(episode, train_reward)
              )
  
          evaluate_reward = run_evaluate_episode(env, agent)
          logger.info(
              "Episode {}, Evaluate reward: {}".format(
                  episode, evaluate_reward
              )
          )
  
file_path: PARL/benchmark/fluid/DDPG/train.py
imports: from parl.utils import check_version_for_fluid
  import argparse
  import gym
  import numpy
  import time
  import parl
  from mujoco_agent import MujocoAgent
  from mujoco_model import MujocoModel
  from parl.utils import logger, ReplayMemory
  from parl.env.continuous_wrappers import ActionMappingWrapper
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types