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

arXiv: https://arxiv.org/abs/2607.06514 · HTML: https://mlsanity.com/paper/2607.06514/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://mlsanity.com/paper/1711.00832/) (2017)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [OpenSpiel: A Framework for Reinforcement Learning in Games](https://mlsanity.com/paper/1908.09453/) (2019)
- [Mastering Complex Control in MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1912.09729/) (2019)
- [Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning](https://mlsanity.com/paper/2206.15378/) (2022)
- [A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games](https://mlsanity.com/paper/2206.05825/) (2022)
- [Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games](https://mlsanity.com/paper/2006.08555/) (2020)
- [EvoJAX: Hardware-Accelerated Neuroevolution](https://mlsanity.com/paper/2202.05008/) (2022)
- [Winning Isn't Everything: Enhancing Game Development with Intelligent Agents](https://mlsanity.com/paper/1903.10545/) (2019)
- [Reevaluating Policy Gradient Methods for Imperfect-Information Games](https://mlsanity.com/paper/2502.08938/) (2025)
- [DareFightingICE Competition: A Fighting Game Sound Design and AI Competition](https://mlsanity.com/paper/2203.01556/) (2022)
- [DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation](https://mlsanity.com/paper/2210.10595/) (2022)
- [EMAgnet: Parameter-Space EMA Regularization for Policy Gradient Self-Play in Large Games](https://mlsanity.com/paper/2606.23995/) (2026)

## Abstract (arXiv)

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.