On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning

Till Freihaut; Giorgia Ramponi

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arXiv · 2411.15046

On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning

Till Freihaut·Giorgia Ramponi

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Abstract

Multi-agent Inverse Reinforcement Learning (MAIRL) aims to recover agent reward functions from expert demonstrations. We characterize the feasible reward set in Markov games, identifying all reward functions that rationalize a given equilibrium. However, equilibrium-based observations are often ambiguous: a single Nash equilibrium can correspond to many reward structures, potentially changing the game's nature in multi-agent systems. We address this by introducing entropy-regularized Markov games, which yield a unique equilibrium while preserving strategic incentives. For this setting, we provide a sample complexity analysis detailing how errors affect learned policy performance. Our work establishes theoretical foundations and practical insights for MAIRL.

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Till Freihaut, Giorgia Ramponi. 2024-11-22. On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning. https://arxiv.org/abs/2411.15046

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