text: import argparse
  
  import numpy as np
  
  from railrl.envs.mujoco.pusher_avoider_3dof import PusherAvoiderEnv3DOF
  from railrl.envs.pygame.water_maze import (
      WaterMaze,
      WaterMaze1D,
      WaterMazeEasy1D,
      WaterMazeMemory1D,
      WaterMazeHard,
  )
  from railrl.exploration_strategies.ou_strategy import OUStrategy
  
  parser = argparse.ArgumentParser()
  parser.add_argument("--small", action='store_true', help="Use a small maze.")
  parser.add_argument("--nreset", default=0, type=int,
                      help="# steps until teleport.")
  parser.add_argument("--H", default=100, type=int, help="env horizon.")
  parser.add_argument("--render", action='store_true', help="Render env.")
  args = parser.parse_args()
  
  # env = WaterMaze()
  env = WaterMazeHard()
  # env = WaterMaze1D()
  # env = WaterMazeMemory1D()
  
  all_returns = []
  es = OUStrategy(env.action_space)
  print(args.H)
  i = 0
  env = PusherAvoiderEnv3DOF(
      task='both',
      init_config=i%5,
  )
  for _ in range(5):
      i += 1
      obs = env.reset()
      es.reset()
      # print("init obs", obs)
      zero_action = np.zeros(3)
      action = zero_action
      last_reward_t = 0
      print("---------- RESET ----------")
      returns = 0
      for t in range(args.H):
          obs, reward, done, info = env.step(action)
          if args.render:
              env.render()
      print("Returns", returns)
      all_returns.append(returns)
      print("Returns Mean", np.mean(all_returns))
      print("Returns Std", np.std(all_returns, axis=0))
  while True:
      pass
  