{"ID":6138306,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T14:51:18.10124973Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07498","arxiv_id":"2607.07498","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.07498","url_pdf":"https://arxiv.org/pdf/2607.07498v1","authors":"[\"Florian Fuchs\",\"Jessy Gosselin-Grant\",\"Boris Skuin\",\"Michele Petteni\",\"Alessandro Sestini\",\"Joakim Bergdahl\",\"Amir Baghi\",\"Linus Gisslén\"]","published":"2026-07-08T14:57:39Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}