Learning Rewards in Strategic Games
Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple agents interact and adapt to one another. This challenge arises in domains such as cybersecurity, autonomous driving, and online markets, where agents' underlying incentives are typically unknown and must be inferred from their observed behavior. Inverse reinforcement learning (IRL) provides a natural framework for this problem, but extending IRL to strategic multi-agent settings introduces a fundamental challenge: a reward that explains observed behavior does not necessarily induce optimal strategies when used for decision-making. In this work, we study this problem through the l...
| Authors | Antoine Bergerault, Cyrille Kone, Giorgia Ramponi |
|---|---|
| Primary category | cs.GT |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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