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.

Hybrid-AIRL addresses a fundamental limitation in reinforcement learning: inferring meaningful reward functions from expert behavior in domains with high uncertainty and sparse feedback. Traditional AIRL struggles when operating in complex environments like Heads-Up Limit Hold'em poker, where delayed outcomes and incomplete information make reward inference difficult. By integrating supervised loss signals derived from expert demonstrations alongside adversarial training, H-AIRL creates a hybrid learning mechanism that grounds the reward inference process in observed expert behavior while maintaining the theoretical benefits of AIRL's adversarial framework.

This advancement contributes to the broader field of imitation learning and reinforcement learning by tackling a practical challenge that limits real-world deployment. Many complex domains—financial trading, game-playing, autonomous systems—share characteristics with poker: sparse rewards, delayed feedback, and significant environmental uncertainty. Current RL approaches often struggle in these settings because agents lack clear signal guidance during training.

The technical innovation of incorporating stochastic regularization alongside supervised signals creates a more robust learning process that converges faster and achieves more stable performance. The research validates H-AIRL across multiple benchmarks while providing reward function visualizations that offer interpretability into learned behaviors. For developers and researchers working on RL applications in challenging domains, this framework offers a practical methodology to accelerate learning convergence and improve policy quality. The work establishes that hybrid approaches combining multiple learning signals outperform purely adversarial methods, informing future directions in imitation learning research and real-world AI system development.

• →Hybrid-AIRL integrates supervised learning signals with adversarial inverse RL to overcome AIRL limitations in complex, imperfect-information environments.
• →The framework demonstrates improved sample efficiency and training stability compared to standard AIRL across Gymnasium benchmarks and poker domains.
• →Stochastic regularization mechanisms enhance reward inference reliability in settings with sparse, delayed feedback.
• →Reward function visualizations provide interpretability into learned behaviors, enabling better understanding of policy development.
• →This hybrid approach has applications across multiple real-world domains requiring learning from expert demonstrations under uncertainty.