用强化学习自动发现游戏守门员漏洞，一次实验找到6种人类数小时才找出的技巧。
Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
- 通过迭代式强化学习训练多只进球智能体，自动搜索游戏漏洞。
- 单次实验发现6种高质量、多样化的进球漏洞策略。
- 适合游戏开发中自动化测试与行为验证，节省人力成本。
游戏测试是游戏行业的一大负担，占用了大量开发预算和人力资源。本文以EA SPORTS NHL 26的开发版本为例，针对守门员人工智能在行为上的漏洞进行测试。为减少每次游戏或行为调整后重复测试的工作量，我们提出一种名为奖励自适应迭代发现（RAID）的新方法，利用迭代强化学习训练一组进球智能体，自动发现漏洞。尽管已有方法能成功发现漏洞，但传统强化学习易过拟合于单一解。我们在此基础上引入简单扩展，使算法能够探索多个多样化且高质量的解决方案。首次部署中，仅一次实验即发现了六种与人工测试数小时发现的策略相似的进球漏洞，显著提升了效率。
原文摘要 · Abstract (English)
Testing is a major effort for the gaming industry, requiring a significant part of development budget and people power. We present a case study on a development version of the ice hockey game EA SPORTS NHL 26, for which human playtesters test the goalie AI for behavioral exploits. To reduce the effort of re-testing the goalie AI after every game or behavior modification in the development phase, we propose Reward-Adaptive Iterative Discovery (RAID), a novel approach to automatically find exploits using an iterative Reinforcement Learning (RL) approach that trains a population of goal scoring agents. While previous approaches can already successfully find exploits, RL algorithms tend to overfit to a single solution. We introduce a simple extension on top of existing RL algorithms, such that they find multiple diverse high-quality solutions. For our first deployment of this approach, within a single experiment we were able to find six hockey scoring exploit strategies that were qualitatively similar to those that playtesters had found in hours-long manual testing sessions.
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