Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization

ArXi:2601.12707v2 Announce Type: replace Estimating the unknown reward functions driving agents' behaviors is of central interest in inverse reinforcement learning and game theory. To tackle this problem, we develop a unified framework for reward function recovery in two-player zero-sum matrix games and Marko games with entropy regularization, where we aim to reconstruct the underlying reward functions given observed players' strategies and actions.