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

arXiv:2511.21356v3 Announce Type: replace-cross Abstract: Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by inferring dense reward functions from expert demonstrations. However, its performance in highly...