Retro Contest
A toolkit for turning classic video games into Gym environments for reinforcement learning research.
What is Retro Contest?
Gym Retro is a Python library developed by OpenAI that converts classic video games into Gym environments for reinforcement learning. It solves the problem of limited and expensive training environments by providing access to approximately 1000 retro games across multiple console platforms, enabling researchers to train and benchmark AI agents in diverse, complex scenarios.
Reinforcement learning researchers, AI developers, and academics who need standardized environments for training and evaluating AI agents using classic video games.
Developers choose Gym Retro for its extensive library of pre-integrated games, seamless compatibility with the Gym API, and the ability to leverage well-understood retro gaming challenges for reproducible reinforcement learning research.
Overview
Retro Games in Gym
Use Cases
Best For
- Training reinforcement learning agents on classic video game challenges
- Benchmarking AI algorithms across diverse retro gaming environments
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