Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26

• URL: http://arxiv.org/abs/2607.07498v1
• Date: Wed, 08 Jul 2026 14:57:39 GMT
• Title: Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
• Abstract summary: 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.<n>We propose Reward-Adaptive Iterative Discovery (RAID), a novel approach to automatically find exploits using an iterative Reinforcement Learning approach.
• Score: 33.50195135603004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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