How to predict in multiprocess environment

I am trying to use a trained model to predict on new data. The problem is that when I use multiple cpu’s (12) I get a matrix of actions instead of one row: I get a row for every process spawned. Do I need to average the results by columns? Is this the correct approach?

    # multiprocess environment
    n_cpu = 12
    env = PortfolioEnv(history=history, abbreviation=instruments, steps=settings['steps'], window_length=settings['window_length'])
    env = SubprocVecEnv([lambda: env for i in range(n_cpu)])

    
    mdl = 'currencies_20050101_20180101_10000_3000_5_return'

    model = PPO2.load(mdl)

    # intialized here
    obs = env.reset()
    states = model.initial_state  # get the initial state vector for the reccurent network
    dones = np.zeros(states.shape[0])  # set all environment to not done
    
    action, _states = model.predict(obs, states, dones)
  
    weights = np.mean(action, axis=0)

[hill-a]

Without multiprocess environment learning is very slow

Yes, the point of multiprocessing is to speed up the learning.

If you want to still use multiprocessing, during prediction you can make observations with zeros except the first observation, then take the first action.

[araffin]

For LSTM policies predict, related: https://github.com/hill-a/stable-baselines/issues/166#issuecomment-502350843