Saraswati/Stable_Baselines3

import gym import numpy as np

from stable_baselines3 import PPO from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv from stable_baselines3.common.env_util import make_vec_env from stable_baselines3.common.utils import set_random_seed

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_random_seed(seed)
return _init

if name == 'main': 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)])

# Stable Baselines provides you with make_vec_env() helper
# which does exactly the previous steps for you.
# You can choose between `DummyVecEnv` (usually faster) and `SubprocVecEnv`
# env = make_vec_env(env_id, n_envs=num_cpu, seed=0, vec_env_cls=SubprocVecEnv)

model = PPO('MlpPolicy', env, verbose=1)
model.learn(total_timesteps=25_000)

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