You would like to use Ray to vectorize your environment but you don't want to use RLLib ?
You came to the right place !

This package allows you to parallelize your environment using Ray
Not only does it allows to run environments in parallel, but it also permits to run multiple sequential environments on each worker
For example, you can run 80 workers in parallel, each running 10 sequential environments for a total of 80 * 10 environments
This can be useful if your environment is fast and simply running 1 environment per worker leads to too much communication overhead between workers

pip install RayEnvWrapper

If something went wrong, it most certainly is because of Ray
For example, you might have issue installing Ray on Apple Silicon (i.e., M1) laptop. See Ray's documentation for a simple fix
At the moment Ray does not support Python 3.10. This package has been tested with Python 3.9.

You first need to define a function that seed and return your environment:

Here is an example for CartPole:

import gym

def make_and_seed(seed: int) -> gym.Env:
    env = gym.make('CartPole-v0')
    env = gym.wrappers.RecordEpisodeStatistics(env) # you can put extra wrapper to your original environment
    env.seed(seed)
    return env

Note: If you don't want to seed your environment, simply return it without using the seed, but the function you define needs to take a number as an input

Then, call the wrapper to create and wrap all the vectorized environment:

from RayEnvWrapper import WrapperRayVecEnv

number_of_workers = 4 # Usually, this is set to the number of CPUs in your machine
envs_per_worker = 2

vec_env = WrapperRayVecEnv(make_and_seed, number_of_workers, envs_per_worker)

You can then use your environment. All the output for each of the environments are stacked in a numpy array

Reset:

vec_env.reset()

Output

[[ 0.03073904  0.00145001 -0.03088818 -0.03131252]
 [ 0.03073904  0.00145001 -0.03088818 -0.03131252]
 [ 0.02281231 -0.02475473  0.02306162  0.02072129]
 [ 0.02281231 -0.02475473  0.02306162  0.02072129]
 [-0.03742824 -0.02316945  0.0148571   0.0296055 ]
 [-0.03742824 -0.02316945  0.0148571   0.0296055 ]
 [-0.0224773   0.04186813 -0.01038048  0.03759079]
 [-0.0224773   0.04186813 -0.01038048  0.03759079]]

The i-th entry represent the initial observation of the i-th environment
Note: As environments are vectorized, you don't need explicitly to reset the environment at the end of the episode, it is done automatically However, you need to do it once at the beginning

Take a random action:

vec_env.step([vec_env.action_space.sample() for _ in range(number_of_workers * envs_per_worker)])

Notice how the actions are passed. We pass an array containing an action for each of the environments
Thus, the array is of size number_of_workers * envs_per_worker (i.e., the total number of environments)

Output

(array([[ 0.03076804, -0.19321568, -0.03151444,  0.25146705],
       [ 0.03076804, -0.19321568, -0.03151444,  0.25146705],
       [ 0.02231721, -0.22019969,  0.02347605,  0.3205903 ],
       [ 0.02231721, -0.22019969,  0.02347605,  0.3205903 ],
       [-0.03789163, -0.21850128,  0.01544921,  0.32693872],
       [-0.03789163, -0.21850128,  0.01544921,  0.32693872],
       [-0.02163994, -0.15310344, -0.00962866,  0.3269806 ],
       [-0.02163994, -0.15310344, -0.00962866,  0.3269806 ]],
      dtype=float32), 
 array([1., 1., 1., 1., 1., 1., 1., 1.], dtype=float32), 
 array([False, False, False, False, False, False, False, False]), 
 [{}, {}, {}, {}, {}, {}, {}, {}])

As usual, the step method returns a tuple, except that here both the observation, reward, dones and infos are concatenated
In this specific example, we have 2 environments per worker.
Index 0 and 1 are environments from worker 1; index 1 and 2 are environments from worker 2, etc.

Apache License 2.0