On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning

Till Freihaut, Giorgia Ramponi

Multi-agent Inverse Reinforcement Learning (MAIRL) aims to recover agent reward functions from expert demonstrations. We characterize the feasible reward set in Markov games, ident…