Pytorch XLA to solve the spawn problems in a Colab Env

As reference only, here is my code

It seems that torch.multiprocessing.set_start_method("spawn") can't be used in an Colab Env. Only 'fork' is allowed.

I have implemented A3C - Data Parallelism to solve the Breakout Atari Game. As I use multi-agents, I need to spawn several processes.

This is representing a single agent :

TotalReward = namedtuple("TotalReward", field_names="reward")

def data_func(net, device, train_queue, batch_size, entropy_beta, 
              env_name, n_envs, gamma, reward_steps, **kwargs):

    env = GymEnvVec(env_name, n_envs)
    agent = Agent(net, batch_size, entropy_beta)
    exp_source = ExperienceSourceFirstLast(env, agent, gamma, reward_steps)

    for exp in exp_source:
        new_rewards = exp_source.pop_total_reward()
        if new_rewards:
            train_queue.put(TotalReward(reward=np.mean(new_rewards)))
        train_queue.put(exp) 

and here is how I set and start several agents

  train_queue = mp.Queue(maxsize=params["process_count"])
  data_proc_list = []

  for _ in range(params["process_count"]):
      data_proc = mp.Process(target=data_func, 
                            args=(net, device, train_queue), 
                            kwargs={**params})
      data_proc.start()
      data_proc_list.append(data_proc)

Can I set multi-agents using Queue and Process in Colab? I have thought using spawn function from here using Pytorch XLA. What do you think?