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'
)
