Playing SNES in the Retro Learning Environment

TL;DR

This paper introduces the Retro Learning Environment (RLE), a new platform for training AI on complex retro video games like SNES and Sega Genesis, expanding beyond Atari benchmarks.

Contribution

The RLE provides an expandable, multi-console environment compatible with Python and Torch, enabling research on more complex games than previous benchmarks.

Findings

RLE supports SNES and Sega Genesis games.

SNES games are more challenging for current algorithms.

RLE maintains compatibility with existing AI frameworks.

Abstract

Mastering a video game requires skill, tactics and strategy. While these attributes may be acquired naturally by human players, teaching them to a computer program is a far more challenging task. In recent years, extensive research was carried out in the field of reinforcement learning and numerous algorithms were introduced, aiming to learn how to perform human tasks such as playing video games. As a result, the Arcade Learning Environment (ALE) (Bellemare et al., 2013) has become a commonly used benchmark environment allowing algorithms to train on various Atari 2600 games. In many games the state-of-the-art algorithms outperform humans. In this paper we introduce a new learning environment, the Retro Learning Environment --- RLE, that can run games on the Super Nintendo Entertainment System (SNES), Sega Genesis and several other gaming consoles. The environment is expandable, allowing for more video games and consoles to be easily added to the environment, while maintaining the same interface as ALE. Moreover, RLE is compatible with Python and Torch. SNES games pose a significant challenge to current algorithms due to their higher level of complexity and versatility.