text: import logging, os
  
  import warnings
  warnings.filterwarnings("ignore")
  
  logging.disable(logging.WARNING)
  os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
  
  import argparse
  import numpy as np
  import tensorflow.compat.v1 as tf
  tf.disable_v2_behavior()
  import time
  import pickle
  
  import maddpg.common.tf_util as U
  from maddpg.trainer.maddpg import MADDPGAgentTrainer
  #from maddpg.trainer.macl import MACLAgentTrainer
  
  
  
  def parse_args():
      parser = argparse.ArgumentParser("Reinforcement Learning experiments for multiagent environments")
      # Environment
      parser.add_argument("--scenario", type=str, default="simple", help="name of the scenario script")
      parser.add_argument("--max-episode-len", type=int, default=25, help="maximum episode length")
      parser.add_argument("--num-episodes", type=int, default=25000, help="number of episodes")
      parser.add_argument("--num-adversaries", type=int, default=0, help="number of adversaries")
      parser.add_argument("--good-policy", type=str, default="maddpg", help="policy for good agents")
      parser.add_argument("--adv-policy", type=str, default="maddpg", help="policy of adversaries")
      parser.add_argument("--good-mic", type=str, default=1e-2, help="mutual information coefficient for good agents")
      parser.add_argument("--adv-mic", type=str, default=0, help="mutual information coefficient for adversaries")
      # Core training parameters
      parser.add_argument("--lr", type=float, default=1e-2, help="learning rate for Adam optimizer")
      parser.add_argument("--gamma", type=float, default=0.95, help="discount factor")
      parser.add_argument("--batch-size", type=int, default=1024, help="number of episodes to optimize at the same time")
      parser.add_argument("--num-units", type=int, default=64, help="number of units in the mlp")
      parser.add_argument("--sleep-regimen", action="store_true", default=False, help="only use mic while sleeping")
      # Checkpointing
      parser.add_argument("--exp-name", type=str, default=None, help="name of the experiment")
      parser.add_argument("--save-dir", type=str, default="./", help="directory in which training state and model should be saved")
      parser.add_argument("--save-rate", type=int, default=100, help="save model once every time this many episodes are completed")
      parser.add_argument("--load-dir", type=str, default="", help="directory in which training state and model are loaded")
      # Evaluation
      parser.add_argument("--restore", action="store_true", default=False)
      parser.add_argument("--display", action="store_true", default=False)
      parser.add_argument("--benchmark", action="store_true", default=False)
      parser.add_argument("--benchmark-iters", type=int, default=10000, help="number of iterations run for benchmarking")
      parser.add_argument("--benchmark-dir", type=str, default="./benchmark_files/", help="directory where benchmark data is saved")
      parser.add_argument("--plots-dir", type=str, default="./learning_curves/", help="directory where plot data is saved")
      return parser.parse_args()
  
  def mlp_model(input, num_outputs, scope, reuse=False, num_units=64, rnn_cell=None):
      # This model takes as input an observation and returns values of all actions
      with tf.variable_scope(scope, reuse=reuse):
          out = input
          out = tf.layers.dense(inputs=out, units=num_units, activation=tf.nn.relu)
          out = tf.layers.dense(inputs=out, units=num_units, activation=tf.nn.relu)
          out = tf.layers.dense(inputs=out, units=num_outputs, activation=None)
          return out
  
  def make_env(scenario_name, arglist, benchmark=False):
      from multiagent.environment import MultiAgentEnv
      import multiagent.scenarios as scenarios
  
      # load scenario from script
      scenario = scenarios.load(scenario_name + ".py").Scenario()
      # create world
      world = scenario.make_world()
      # create multiagent environment
      if benchmark:
          env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, scenario.benchmark_data)
      else:
          env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation)
      return env
  
  def get_trainers(env, num_adversaries, obs_shape_n, arglist):
      trainers = []
      model = mlp_model
      trainer = MADDPGAgentTrainer
  
      for i in range(num_adversaries):
          trainers.append(trainer(
              "agent_%d" % i, model, obs_shape_n, env.action_space, i, arglist, agent_type="adversary",
              local_q_func=(arglist.adv_policy=='ddpg')))
      for i in range(num_adversaries, env.n):
          trainers.append(trainer(
              "agent_%d" % i, model, obs_shape_n, env.action_space, i, arglist, agent_type="good",
              local_q_func=(arglist.good_policy=='ddpg')))
      return trainers
  
  
  def train(arglist):
      with U.single_threaded_session():
          # Create environment
          env = make_env(arglist.scenario, arglist, arglist.benchmark)
          # Create agent trainers
          obs_shape_n = [env.observation_space[i].shape for i in range(env.n)]
  
          num_adversaries = min(env.n, arglist.num_adversaries)
          trainers = get_trainers(env, num_adversaries, obs_shape_n, arglist)
          print('Using good policy {} and adv policy {}'.format(arglist.good_policy, arglist.adv_policy))
  
          # Initialize
          U.initialize()
  
          # Load previous results, if necessary
          if arglist.load_dir == "":
              arglist.load_dir = arglist.save_dir
          if arglist.display or arglist.restore or arglist.benchmark:
              print('Loading previous state...')
              U.load_state(arglist.load_dir)
  
          episode_rewards = [0.0]  # sum of rewards for all agents
          agent_rewards = [[0.0] for _ in range(env.n)]  # individual agent reward
          final_ep_rewards = []  # sum of rewards for training curve
          final_ep_ag_rewards = []  # agent rewards for training curve
          agent_info = [[[]]]  # placeholder for benchmarking info
          saver = tf.train.Saver()
          obs_n = env.reset()
          episode_step = 0
          train_step = 0
          t_start = time.time()
  
          print('Starting iterations...')
          while True:
              # get action
              action_n = [agent.action(obs) for agent, obs in zip(trainers,obs_n)]
              # environment step
              new_obs_n, rew_n, done_n, info_n = env.step(action_n)
              episode_step += 1
              done = all(done_n)
              terminal = (episode_step >= arglist.max_episode_len)
              # collect experience
              for i, agent in enumerate(trainers):
                  agent.experience(obs_n[i], action_n[i], rew_n[i], new_obs_n[i], done_n[i], terminal)
              obs_n = new_obs_n
  
              for i, rew in enumerate(rew_n):
                  episode_rewards[-1] += rew
                  agent_rewards[i][-1] += rew
  
              if done or terminal:
                  obs_n = env.reset()
                  episode_step = 0
                  episode_rewards.append(0)
                  for a in agent_rewards:
                      a.append(0)
                  agent_info.append([[]])
  
              # increment global step counter
              train_step += 1
  
              # for benchmarking learned policies
              if arglist.benchmark:
                  for i, info in enumerate(info_n):
                      agent_info[-1][i].append(info_n['n'])
                  if train_step > arglist.benchmark_iters and (done or terminal):
                      file_name = arglist.benchmark_dir + arglist.exp_name + '.pkl'
                      print('Finished benchmarking, now saving...')
                      with open(file_name, 'wb') as fp:
                          pickle.dump(agent_info[:-1], fp)
                      break
                  continue
  
              # for displaying learned policies
              if arglist.display:
                  time.sleep(0.1)
                  env.render()
                  continue
  
              # update all trainers, if not in display or benchmark mode
              loss = None
              for agent in trainers:
                  agent.preupdate()
              for agent in trainers:
                  loss = agent.update(trainers, train_step)
  
                  if(loss is not None and agent.sleep_regimen and agent.agent_mic != 0 and train_step % 100 == 0): # Change sleep frequency here if desired
                      original_policy_loss = loss[1]
                      new_loss = agent.update(trainers, train_step, sleeping=True)[1]
                      sleep_iteration = 0
                      while((sleep_iteration < 10) and (new_loss < original_policy_loss * 1.05)):
                          new_loss = agent.update(trainers, train_step, sleeping=True)[1]
                          sleep_iteration += 1 
                          #print("sleep walking")
  
              # save model, display training output
              if terminal and (len(episode_rewards) % arglist.save_rate == 0):
                  U.save_state(arglist.save_dir, saver=saver)
                  # print statement depends on whether or not there are adversaries
                  if num_adversaries == 0:
                      print("steps: {}, episodes: {}, mean episode reward: {}, time: {}".format(
                          train_step, len(episode_rewards), np.mean(episode_rewards[-arglist.save_rate:]), round(time.time()-t_start, 3)))
                  else:
                      print("steps: {}, episodes: {}, mean episode reward: {}, agent episode reward: {}, time: {}".format(
                          train_step, len(episode_rewards), np.mean(episode_rewards[-arglist.save_rate:]),
                          [np.mean(rew[-arglist.save_rate:]) for rew in agent_rewards], round(time.time()-t_start, 3)))
                  t_start = time.time()
                  # Keep track of final episode reward
                  final_ep_rewards.append(np.mean(episode_rewards[-arglist.save_rate:]))
                  for rew in agent_rewards:
                      final_ep_ag_rewards.append(np.mean(rew[-arglist.save_rate:]))
  
              # saves final episode reward for plotting training curve later
              if len(episode_rewards) > arglist.num_episodes:
                  print(arglist.plots_dir)
                  print(arglist.exp_name)
  
                  rew_file_name = arglist.plots_dir + arglist.exp_name + '_rewards.pkl'
                  with open(rew_file_name, 'wb') as fp:
                      pickle.dump(final_ep_rewards, fp)
                  agrew_file_name = arglist.plots_dir + arglist.exp_name + '_agrewards.pkl'
                  with open(agrew_file_name, 'wb') as fp:
                      pickle.dump(final_ep_ag_rewards, fp)
                  print('...Finished total of {} episodes.'.format(len(episode_rewards)))
                  break
  
  if __name__ == '__main__':
      arglist = parse_args()
      train(arglist)
  