retrowrapper

Wrapper for OpenAI Retro envs for parallel execution

OpenAI’s Retro exposes an OpenAI gym interface for Deep Reinforcement Learning, but
unfortunately, their back-end only allows one emulator instance per process. To get around this, I wrote this class.

To Use

To use it, just instantiate it like you would a normal retro environment, and then treat it exactly the same, but now you can have multiples in a single python process. Magic!

import retrowrapper
if __name__ == "__main__":
    game = "SonicTheHedgehog-Genesis"
    state = "GreenHillZone.Act1"
    env1 = retrowrapper.RetroWrapper(game, state=state)
    env2 = retrowrapper.RetroWrapper(game, state=state)
    _obs = env1.reset()
    _obs = env2.reset()
    done = False
    while not done:
        action = env1.action_space.sample()
        _obs, _rew, done, _info = env1.step(action)
        env1.render()
        action = env2.action_space.sample()
        _obs, _rew, done, _info = env2.step(action)
        env2.render()

Using a custom make function

Sometimes you will need a custom make function, for example the retro_contest
repository requires you to use their make function rather than retro.make.

In these cases you can use the retrowrapper.set_retro_make() to set a new
make function.

Example usage:

import retrowrapper
from retro_contest.local import make
retrowrapper.set_retro_make( make )
env1 = retrowrapper.RetroWrapper(
    game='SonicTheHedgehog2-Genesis', 
    state='MetropolisZone.Act1'
)
env2 = retrowrapper.RetroWrapper(
    game='SonicTheHedgehog2-Genesis', 
    state='MetropolisZone.Act2'
)