https://github.com/maxstrange/retrowrapper

Wrapper for OpenAI Retro envs for parallel execution
https://github.com/maxstrange/retrowrapper

deep-learning openai-gym reinforcement-learning retro

Last synced: 3 months ago
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Wrapper for OpenAI Retro envs for parallel execution

• Host: GitHub
• URL: https://github.com/maxstrange/retrowrapper
• Owner: MaxStrange
• License: mit
• Created: 2018-05-18T02:35:54.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-12-22T04:17:50.000Z (over 7 years ago)
• Last Synced: 2025-03-29T19:11:29.583Z (about 1 year ago)
• Topics: deep-learning, openai-gym, reinforcement-learning, retro
• Language: Python
• Size: 9.77 KB
• Stars: 27
• Watchers: 2
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# retrowrapper

[![Build Status](https://travis-ci.org/MaxStrange/retrowrapper.svg?branch=master)](https://travis-ci.org/MaxStrange/retrowrapper)

Wrapper for OpenAI Retro envs for parallel execution

OpenAI's [Retro](https://github.com/openai/retro) exposes an OpenAI [gym](https://gym.openai.com/) 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!

```python

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:

```python

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'

)

```