# Towards Game-Playing AI Benchmarks via Performance Reporting Standards

arXiv: https://arxiv.org/abs/2007.02742 · HTML: https://mlsanity.com/paper/2007.02742/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning](https://mlsanity.com/paper/2105.14780/) (2021)
- [A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks](https://mlsanity.com/paper/2506.02883/) (2025)
- [A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning](https://mlsanity.com/paper/2209.00711/) (2022)

## Abstract (arXiv)

While games have been used extensively as milestones to evaluate game-playing AI, there exists no standardised framework for reporting the obtained observations. As a result, it remains difficult to draw general conclusions about the strengths and weaknesses of different game-playing AI algorithms. In this paper, we propose reporting guidelines for AI game-playing performance that, if followed, provide information suitable for unbiased comparisons between different AI approaches. The vision we describe is to build benchmarks and competitions based on such guidelines in order to be able to draw more general conclusions about the behaviour of different AI algorithms, as well as the types of challenges different games pose.