text: r"""Running a pre-trained ppo agent on minitaur_reactive_env."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import time
  
  import inspect
  currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
  parentdir = os.path.dirname(os.path.dirname(os.path.dirname(currentdir)))
  print("parentdir=",parentdir)
  os.sys.path.insert(0,parentdir)
  
  
  
  import tensorflow as tf
  from pybullet_envs.minitaur.agents.scripts import utility
  import pybullet_data
  from pybullet_envs.minitaur.envs import simple_ppo_agent
  
  
  flags = tf.app.flags
  FLAGS = tf.app.flags.FLAGS
  LOG_DIR = os.path.join(pybullet_data.getDataPath(), "policies/ppo/minitaur_reactive_env")
  CHECKPOINT = "model.ckpt-14000000"
  
  
  def main(argv):
    del argv  # Unused.
    config = utility.load_config(LOG_DIR)
    policy_layers = config.policy_layers
    value_layers = config.value_layers
    env = config.env(render=True)
    network = config.network
  
    with tf.Session() as sess:
      agent = simple_ppo_agent.SimplePPOPolicy(
          sess,
          env,
          network,
          policy_layers=policy_layers,
          value_layers=value_layers,
          checkpoint=os.path.join(LOG_DIR, CHECKPOINT))
  
      sum_reward = 0
      observation = env.reset()
      while True:
        action = agent.get_action([observation])
        observation, reward, done, _ = env.step(action[0])
        time.sleep(0.002)
        sum_reward += reward
        if done:
          break
      tf.logging.info("reward: %s", sum_reward)
  
  
  if __name__ == "__main__":
    tf.app.run(main)
  