Learning Rewards in Strategic Games
Read the original on arXiv AI →The paper investigates how to infer agents’ underlying incentives in strategic multi‑agent settings, where traditional inverse reinforcement learning (IRL) may fail to produce rewards that guide optimal play. It introduces the concept of strategic identifiability and shows that learning from a fixed pair of policies can be insufficient regardless of sample size. To address this, the authors propose an active IRL algorithm for two‑player repeated Stackelberg games, combining maximum‑likelihood estimation with experimental design to achieve finite‑sample guarantees on sub‑optimality, and validate the approach in an anti‑poaching scenario.
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