For How Long Should We Be Punching? Learning Action Duration in Fighting Games
A new study explores how reinforcement learning agents can improve their performance in fighting games by learning not only which actions to take but also how long to execute them. The research shows that agents trained with this method can adapt their responsiveness to various game situations. Results indicate that while learned timing can enhance performance, it does not guarantee robustness on its own.
- ▪Fighting games present unique challenges for reinforcement learning agents due to their fast-paced nature.
- ▪The study proposes a framework where agents learn both the action and its duration, allowing for dynamic adaptability.
- ▪Experiments demonstrate that agents perform best with high frame skip values, which simplifies the learning of exploitative strategies.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20911 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Artificial Intelligence arXiv:2605.20911 (cs) [Submitted on 20 May 2026] Title:For How Long Should We Be Punching? Learning Action Duration in Fighting Games Authors:Hoang Hai Nguyen, Kurt Driessens, Dennis J.N.J. Soemers View a PDF of the paper titled For How Long Should We Be Punching? Learning Action Duration in Fighting Games, by Hoang Hai Nguyen and Kurt Driessens and Dennis J.N.J. Soemers View PDF HTML (experimental) Abstract:Fighting games such as Street Fighter II present unique challenges to reinforcement learning (RL) agents due to their fast-paced, real-time nature. In most RL frameworks, agents are hard-coded to make decisions at a fixed interval, typically every frame or every N frames.
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