FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games

2026-07-07

AI Summary

This work addresses the lack of efficient, open-source reinforcement learning environments tailored to nontrivial two-player zero-sum imperfect-information games. It introduces Footsies, an open-source RL environment based on a minimalist 2D fighting game that, for the first time, distills core strategic mechanisms from real fighting-game neutral phases—such as cyclicity and non-transitivity—into an analytically tractable and computationally efficient benchmark. Implemented in Python with a vectorized simulator, Footsies enables high-throughput training on standard hardware and includes integrated support for systematic evaluation across multiple reinforcement learning algorithms. By balancing strategic complexity with scalability, Footsies establishes a reproducible and accessible benchmark for advancing research in imperfect-information games.

Abstract

We present FootsiesGym, an open-source environment for learning in a non-trivial two-player, zero-sum, imperfect-information game. Built on HiFight's minimalist 2D fighting game Footsies, it isolates the cyclic, non-transitive strategic interactions of fighting game neutral play while remaining simple enough for efficient analysis. We provide a vectorized simulator that enables high-throughput training on standard hardware, making the environment accessible and reproducible. We describe the design of the environment, benchmark several reinforcement learning algorithms, and discuss open research directions it enables. The code is available at https://github.com/como-research/FootsiesGym.

Problem

• two-player
• zero-sum
• imperfect-information
• fighting game
• benchmark

Innovation

• imperfect-information games
• two-player zero-sum
• non-transitive dynamics
• vectorized simulator
• reinforcement learning benchmark