text: import sys
  import os
  sys.path.append(os.getcwd()+'/ReinforcementLearning')
  import MyGym
  import gym
  import time
  
  def random_action_test(args):
      count = 0
      if args.render:
          env = gym.make(args.env, render=True)
      else:
          env = gym.make(args.env)
      env.reset()
      sample = env.action_space.sample()
      action = sample
      count = 0
      for i in range(args.steps):
          action = env.action_space.sample()
          action = [0]*len(action)
          obs, rewards, done, _ = env.step(action)
          if done:
              obs = env.reset()
          time.sleep(0.016)
  
  def main():
  
      import argparse
      parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
      parser.add_argument('--env', help='environment ID')
      parser.add_argument('--render', help='OpenGL Visualizer', type=bool, default=True)
      parser.add_argument('--steps', help='Number of steps', type=int, default=5000)
  
      args = parser.parse_args()
  
      random_action_test(args)
  
  if __name__ == '__main__':
      main()
  