code: import argparse
  import time
  import os
  import logging
  from baselines import logger, bench
  from baselines.common.misc_util import (
      set_global_seeds,
      boolean_flag,
  )
  #import baselines.ddpg.training as training
  import training as training
  from baselines.ddpg.models import Actor, Critic
  from baselines.ddpg.memory import Memory
  from baselines.ddpg.noise import *
  
  import gym
  import tensorflow as tf
  from mpi4py import MPI
  
  def run(env_id, seed, noise_type, layer_norm, evaluation, **kwargs):
      # Configure things.
      rank = MPI.COMM_WORLD.Get_rank()
      if rank != 0:
          logger.set_level(logger.DISABLED)
  
      # Create envs.
      env = gym.make(env_id)
      env = bench.Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
  
      if evaluation and rank==0:
          eval_env = gym.make(env_id)
          eval_env = bench.Monitor(eval_env, os.path.join(logger.get_dir(), 'gym_eval'))
          #env = bench.Monitor(env, None)
      else:
          eval_env = None
  
      # Parse noise_type
      action_noise = None
      param_noise = None
      nb_actions = env.action_space.shape[-1]
      for current_noise_type in noise_type.split(','):
          current_noise_type = current_noise_type.strip()
          if current_noise_type == 'none':
              pass
          elif 'adaptive-param' in current_noise_type:
              _, stddev = current_noise_type.split('_')
              param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
          elif 'normal' in current_noise_type:
              _, stddev = current_noise_type.split('_')
              action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
          elif 'ou' in current_noise_type:
              _, stddev = current_noise_type.split('_')
              action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
          else:
              raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
  
      # Configure components.
      memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
      critic = Critic(layer_norm=layer_norm)
      actor = Actor(nb_actions, layer_norm=layer_norm)
  
      # Seed everything to make things reproducible.
      seed = seed + 1000000 * rank
      logger.info('rank {}: seed={}, logdir={}'.format(rank, seed, logger.get_dir()))
      tf.reset_default_graph()
      set_global_seeds(seed)
      env.seed(seed)
      if eval_env is not None:
          eval_env.seed(seed)
  
      # Disable logging for rank != 0 to avoid noise.
      if rank == 0:
          start_time = time.time()
      training.train(env=env, eval_env=eval_env, param_noise=param_noise,
          action_noise=action_noise, actor=actor, critic=critic, memory=memory, **kwargs)
      env.close()
      if eval_env is not None:
          eval_env.close()
      if rank == 0:
          logger.info('total runtime: {}s'.format(time.time() - start_time))
  
  
  def parse_args():
      parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  
      parser.add_argument('--env-id', type=str, default='HalfCheetah-v2')
      boolean_flag(parser, 'render-eval', default=False)
      boolean_flag(parser, 'layer-norm', default=True)
      boolean_flag(parser, 'render', default=False)
      boolean_flag(parser, 'normalize-returns', default=False)
      boolean_flag(parser, 'normalize-observations', default=True)
      parser.add_argument('--seed', help='RNG seed', type=int, default=0)
      parser.add_argument('--critic-l2-reg', type=float, default=1e-2)
      parser.add_argument('--batch-size', type=int, default=64)  # per MPI worker
      parser.add_argument('--actor-lr', type=float, default=1e-4)
      parser.add_argument('--critic-lr', type=float, default=1e-3)
      boolean_flag(parser, 'popart', default=False)
      parser.add_argument('--gamma', type=float, default=0.99)
      parser.add_argument('--reward-scale', type=float, default=1.)
      parser.add_argument('--clip-norm', type=float, default=None)
      parser.add_argument('--nb-epochs', type=int, default=500)  # with default settings, perform 1M steps total, was 500
      parser.add_argument('--nb-epoch-cycles', type=int, default=20)
      parser.add_argument('--nb-train-steps', type=int, default=50)  # per epoch cycle and MPI worker
      parser.add_argument('--nb-eval-steps', type=int, default=1000)  # per epoch cycle and MPI worker
      parser.add_argument('--nb-rollout-steps', type=int, default=100)  # per epoch cycle and MPI worker
      parser.add_argument('--noise-type', type=str, default='adaptive-param_0.2')  # choices are adaptive-param_xx, ou_xx, normal_xx, none
      parser.add_argument('--num-timesteps', type=int, default=None)
      parser.add_argument('--alg', type=str, default='DDPG') # DDPG or DDPGRM
      #boolean_flag(parser, 'evaluation', default=False)
      
      boolean_flag(parser, 'evaluation', default=True) #turn evaluation on 
      args = parser.parse_args()
      # we don't directly specify timesteps for this script, so make sure that if we do specify them
      # they agree with the other parameters. default: 1M total steps
      
  
      eval_steps_per_epoch = args.nb_epoch_cycles*args.nb_eval_steps #defualt: 1000*20 = 10K (~ 20 episodes)
      print(args)
      if args.num_timesteps is not None:
          assert(args.num_timesteps == args.nb_epochs * args.nb_epoch_cycles * args.nb_rollout_steps)
      dict_args = vars(args)
      del dict_args['num_timesteps']
      return dict_args
  
  
  if __name__ == '__main__':
      args = parse_args()
      if MPI.COMM_WORLD.Get_rank() == 0:
          logger.configure()
      # Run actual script.
      run(**args)
  
apis: tensorflow.reset_default_graph