arXiv:2607.06514v1 Announce Type: new 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 reinforceme
The continuous development in AI research necessitates robust and accessible benchmarks for specific and complex problems like imperfect-information games.
This development provides a significant tool for advancing AI agents, particularly in understanding and navigating strategic interactions in competitive, uncertain environments, which has implications beyond gaming.
The availability of an open-source, high-throughput environment for training AI in complex two-player, zero-sum, imperfect-information games accelerates research and development in this area.
- · AI researchers
- · AI agent developers
- · Reinforcement learning platforms
Improved performance and sophistication of AI models in strategic, competitive scenarios.
Potential for AI breakthroughs in fields requiring robust strategic decision-making with imperfect information, like cybersecurity or finance.
Increased accessibility of advanced AI training tools could democratize development, leading to a wider array of AI applications.
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