Function main

rl2/mountaincar/pg_tf.py:202–236

()

def main():
  env = gym.make('MountainCarContinuous-v0')
  ft = FeatureTransformer(env, n_components=100)
  D = ft.dimensions
  pmodel = PolicyModel(D, ft, [])
  vmodel = ValueModel(D, ft, [])
  init = tf.global_variables_initializer()
  session = tf.InteractiveSession()
  session.run(init)
  pmodel.set_session(session)
  vmodel.set_session(session)
  gamma = 0.95

  if 'monitor' in sys.argv:
    filename = os.path.basename(__file__).split('.')[0]
    monitor_dir = './' + filename + '_' + str(datetime.now())
    env = wrappers.Monitor(env, monitor_dir)

  N = 50
  totalrewards = np.empty(N)
  costs = np.empty(N)
  for n in range(N):
    totalreward, num_steps = play_one_td(env, pmodel, vmodel, gamma)
    totalrewards[n] = totalreward
    if n % 1 == 0:
      print("episode:", n, "total reward: %.1f" % totalreward, "num steps: %d" % num_steps, "avg reward (last 100): %.1f" % totalrewards[max(0, n-100):(n+1)].mean())

  print("avg reward for last 100 episodes:", totalrewards[-100:].mean())

  plt.plot(totalrewards)
  plt.title("Rewards")
  plt.show()

  plot_running_avg(totalrewards)
  plot_cost_to_go(env, vmodel)


if __name__ == '__main__':