text: import gym
  from baselines.vpg import vpg
  import tensorflow as tf
  
  env = gym.envs.make("MountainCarContinuous-v0")
  tf.reset_default_graph()
  
  global_step = tf.Variable(0, name="global_step", trainable=False)
  policy_estimator = vpg.PolicyEstimator(env, learning_rate=0.001)
  value_estimator = vpg.ValueEstimator(env, learning_rate=0.1)
  
  with tf.Session() as sess:
      sess.run(tf.initialize_all_variables())
      # Note, due to randomness in the policy the number of episodes you need varies
      # g = tf.Graph()
      # with g.as_default():
      #     for i in range(10):
      policy_estimator = vpg.learn(env, policy_estimator, value_estimator, max_timesteps=10000,
                      discount_factor=0.95,
                      print_freq=1,
                      outdir="/tmp/experiments/continuous/VPG/other")
  
      # plotting.plot_episode_stats(stats, smoothing_window=10)
  
      # # Try it out
      # state = env.reset()
      # while 1:
      #     env.render()
      #     action = policy_estimator.predict(state)
      #     next_state, reward, done, _ = env.step(action)
      #     if done:
      #         state = env.reset()
      #     state = next_state
  