Why does env.render() create multiple render screens? | LSTM policy predict with one env [question]

When I run the code example from the docs for cartpole multiprocessing, it renders one window with all env’s playing the game. It also renders individual windows with the same env’s playing the same games.

import gym
import numpy as np

from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import SubprocVecEnv
from stable_baselines.common import set_global_seeds
from stable_baselines import ACKTR

def make_env(env_id, rank, seed=0):
    """
    Utility function for multiprocessed env.

    :param env_id: (str) the environment ID
    :param num_env: (int) the number of environments you wish to have in subprocesses
    :param seed: (int) the inital seed for RNG
    :param rank: (int) index of the subprocess
    """
    def _init():
        env = gym.make(env_id)
        env.seed(seed + rank)
        return env
    set_global_seeds(seed)
    return _init

env_id = "CartPole-v1"
num_cpu = 4  # Number of processes to use
# Create the vectorized environment
env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])

model = ACKTR(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=25000)

obs = env.reset()
for _ in range(1000):
    action, _states = model.predict(obs)
    obs, rewards, dones, info = env.step(action)
    env.render()

System Info Describe the characteristic of your environment:

• Vanilla install, followed the docs using pip
• gpus: 2-gtx-1080ti’s
• Python version 3.6.5
• Tensorflow version 1.12.0
• ffmpeg 4.0

Additional context

[hn2]

n_env = 12 env = PortfolioEnv(history=history, abbreviation=instruments, steps=settings[‘steps’], window_length=settings[‘window_length’], include_ta=settings[‘include_ta’],allow_short=settings[‘allow_short’], reward=settings[‘reward’], debug=settings[‘debug’]) env = SubprocVecEnv([lambda: env for _ in range(1)])

mdl = 'ES_19900102_20180101_5000000_7000_1_return_False_7a686c53e4a34338942a8b4bbe65fa47'
model = PPO2.load(mdl)

# intialized here
obs = env.reset()
zero_completed_obs = np.zeros((n_env,) + env.observation_space.shape)
zero_completed_obs[0, :] = obs

state = None
state = model.initial_state   
done = np.zeros(state.shape[0])   

pos, state = model.predict(zero_completed_obs, state, done)

Traceback (most recent call last): File “C:\Users\hanna\Anaconda3\lib\site-packages\quantiacsToolbox\quantiacsToolbox.py”, line 871, in runts position, settings = TSobject.myTradingSystem(*argList) File “ppo2_quantiacs_test.py”, line 68, in myTradingSystem pos, state = model.predict(zero_completed_obs, state, done) File “C:\Users\hanna\Anaconda3\lib\site-packages\stable_baselines\common\base_class.py”, line 469, in predict vectorized_env = self._is_vectorized_observation(observation, self.observation_space) File “C:\Users\hanna\Anaconda3\lib\site-packages\stable_baselines\common\base_class.py”, line 399, in _is_vectorized_observation .format(", ".join(map(str, observation_space.shape)))) ValueError: Error: Unexpected observation shape (12, 5) for Box environment, please use (10,) or (n_env, 10) for the observation shape.

[hill-a]

Still have a problem in

pos, state = model.predict(zero_completed_obs, state, done)

ValueError: Error: Unexpected observation shape (12, 5) for Box environment, please use (10,) or (n_env, 10) for the observation shape.

Model was trained with n_env = 12

Where this 10 comes from?

A few things:

• This is not related to the issue.
• You did not give the required code to replicate the issue.
• You did not give the full stack trace (which could be used to help find the origin of the issue)
• please use the Markdown highlighting code format (https://help.github.com/en/articles/creating-and-highlighting-code-blocks)

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