text: # https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence
  import numpy as np
  from multiprocessing import Process, Pipe
  
  
  def worker(remote, parent_remote, env_fn_wrapper):
      parent_remote.close()
      env = env_fn_wrapper.x()
      while True:
          cmd, data = remote.recv()
          if cmd == 'step':
              ob, reward, done, info = env.step(data)
              if done:
                  ob = env.reset()
              remote.send((ob, reward, done, info))
          elif cmd == 'reset':
              ob = env.reset()
              remote.send(ob)
          elif cmd == 'reset_task':
              ob = env.reset_task()
              remote.send(ob)
          elif cmd == 'close':
              remote.close()
              break
          elif cmd == 'get_spaces':
              remote.send((env.action_space, env.observation_space))
          elif cmd == 'get_id':
              remote.send(env.spec.id)
          else:
              raise NotImplementedError
  
  
  class CloudpickleWrapper():
      """
      Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
      """
  
      def __init__(self, x):
          self.x = x
  
      def __getstate__(self):
          import cloudpickle
          return cloudpickle.dumps(self.x)
  
      def __setstate__(self, ob):
          import pickle
          self.x = pickle.loads(ob)
  
  
  class SubprocVecEnv():
      def __init__(self, env_fns):
          """
          envs: list of gym environments to run in subprocesses
          """
          self.closed = False
          nenvs = len(env_fns)
          self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
          self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
                     for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
          for p in self.ps:
              p.daemon = True  # if the main process crashes, we should not cause things to hang
              p.start()
          for remote in self.work_remotes:
              remote.close()
  
          self.remotes[0].send(('get_spaces', None))
          self.action_space, self.observation_space = self.remotes[0].recv()
  
          self.remotes[0].send(('get_id', None))
          self.env_id = self.remotes[0].recv()
  
      def step(self, actions):
          for remote, action in zip(self.remotes, actions):
              remote.send(('step', action))
          results = [remote.recv() for remote in self.remotes]
          obs, rews, dones, infos = zip(*results)
          # print("Infos:", infos)
          # for done, info in zip(dones, infos):
          #     if done:
          #         # print("Total reward:", info['reward'], "Num steps:", info['episode_length'])
          #         print("Returned info:", info, "Done:", done)
          return np.stack(obs), np.stack(rews), np.stack(dones), infos
  
      def reset(self):
          for remote in self.remotes:
              remote.send(('reset', None))
          return np.stack([remote.recv() for remote in self.remotes])
  
      def reset_task(self):
          for remote in self.remotes:
              remote.send(('reset_task', None))
          return np.stack([remote.recv() for remote in self.remotes])
  
      def close(self):
          if self.closed:
              return
  
          for remote in self.remotes:
              remote.send(('close', None))
          for p in self.ps:
              p.join()
          self.closed = True
  
      @property
      def num_envs(self):
          return len(self.remotes)
  