Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance

Adversarial Inverse Reinforcement Learning (AIRL) addresses sparse rewards by inferring dense reward functions from expert demonstrations, but its performance in complex, imperfect-information settings is underexplored.We evaluate AIRL in Heads-Up Limit Hold'em (HULHE) poker and observe that it faces challenges producing sufficiently informative rewards.To address this, we introduce Hybrid-AIRL (H-AIRL), which improves reward inference and policy learning using a partially supervised loss from expert data and stochastic regularization.Experiments on Gymnasium benchmarks and HULHE poker show that H-AIRL improves sample efficiency and training stability, highlighting the value of supervised signals in inverse RL.