AINeutralarXiv – CS AI · Apr 155/10
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Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance
Researchers introduce Hybrid-AIRL, an enhanced inverse reinforcement learning framework that combines adversarial learning with supervised expert guidance to improve reward function inference in complex, imperfect-information environments like poker. The method demonstrates superior sample efficiency and learning stability compared to traditional AIRL, particularly in settings with sparse and delayed rewards.