For How Long Should We Be Punching? Learning Action Duration in Fighting Games
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. Although this design ensures timely responses, it restricts the agent's ability to adjust its reaction timing. Acting every frame grants frame-perfect reflexes, which are unrealistic compared to human players, whereas longer fixed intervals reduce computational cost but hinder responsiveness. We consider an alternative decision-making framework in which the agent learns not only what action to take but also for how long ...
| Authors | Hoang Hai Nguyen, Kurt Driessens, Dennis J. N. J. Soemers |
|---|---|
| Primary category | cs.AI |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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