old_contents: #!/usr/bin/env python
  
  """
  Code to load an expert policy and save out data for behavioral cloning.
  Example usage:
      python run_expert.py experts/humanoid.pkl Humanoid-v1 --render \
              --output_file expert_data.pkl --num_rollouts 20
  
  Author of this script and included expert policies: Jonathan Ho (hoj@openai.com)
  """
  
  import pickle
  import tensorflow as tf
  import numpy as np
  import tf_util
  import gym
  import load_policy
  
  def main():
      import argparse
      parser = argparse.ArgumentParser()
      parser.add_argument('expert_policy_file', type=str)
      parser.add_argument('envname', type=str)
      parser.add_argument('--render', action='store_true')
      parser.add_argument("--max_timesteps", type=int)
      parser.add_argument('--num_rollouts', type=int, default=20,
                          help='Number of expert roll outs')
      args = parser.parse_args()
  
      print('loading and building expert policy')
      policy_fn = load_policy.load_policy(args.expert_policy_file)
      print('loaded and built')
  
      with tf.Session():
          tf_util.initialize()
  
          import gym
          env = gym.make(args.envname)
          max_steps = args.max_timesteps or env.spec.timestep_limit
  
          returns = []
          observations = []
          actions = []
          for i in range(args.num_rollouts):
              print('iter', i)
              obs = env.reset()
              done = False
              totalr = 0.
              steps = 0
              while not done:
                  action = policy_fn(obs[None,:])
                  observations.append(obs)
                  actions.append(action)
                  obs, r, done, _ = env.step(action)
                  totalr += r
                  steps += 1
                  if args.render:
                      env.render()
                  if steps % 100 == 0: print("%i/%i"%(steps, max_steps))
                  if steps >= max_steps:
                      break
              returns.append(totalr)
  
          print('returns', returns)
          print('mean return', np.mean(returns))
          print('std of return', np.std(returns))
  
          expert_data = {'observations': np.array(observations),
                         'actions': np.array(actions)}
  
  if __name__ == '__main__':
      main()
  
new_contents: #!/usr/bin/env python
  
  """
  Code to load an expert policy and generate roll-out data for behavioral cloning.
  Example usage:
      python run_expert.py experts/humanoid.pkl Humanoid-v1 --render \
              --num_rollouts 20
  
  Author of this script and included expert policies: Jonathan Ho (hoj@openai.com)
  """
  
  import pickle
  import tensorflow as tf
  import numpy as np
  import tf_util
  import gym
  import load_policy
  
  def main():
      import argparse
      parser = argparse.ArgumentParser()
      parser.add_argument('expert_policy_file', type=str)
      parser.add_argument('envname', type=str)
      parser.add_argument('--render', action='store_true')
      parser.add_argument("--max_timesteps", type=int)
      parser.add_argument('--num_rollouts', type=int, default=20,
                          help='Number of expert roll outs')
      args = parser.parse_args()
  
      print('loading and building expert policy')
      policy_fn = load_policy.load_policy(args.expert_policy_file)
      print('loaded and built')
  
      with tf.Session():
          tf_util.initialize()
  
          import gym
          env = gym.make(args.envname)
          max_steps = args.max_timesteps or env.spec.timestep_limit
  
          returns = []
          observations = []
          actions = []
          for i in range(args.num_rollouts):
              print('iter', i)
              obs = env.reset()
              done = False
              totalr = 0.
              steps = 0
              while not done:
                  action = policy_fn(obs[None,:])
                  observations.append(obs)
                  actions.append(action)
                  obs, r, done, _ = env.step(action)
                  totalr += r
                  steps += 1
                  if args.render:
                      env.render()
                  if steps % 100 == 0: print("%i/%i"%(steps, max_steps))
                  if steps >= max_steps:
                      break
              returns.append(totalr)
  
          print('returns', returns)
          print('mean return', np.mean(returns))
          print('std of return', np.std(returns))
  
          expert_data = {'observations': np.array(observations),
                         'actions': np.array(actions)}
  
  if __name__ == '__main__':
      main()
  
subject: update usage doc