Interpretable Personalized Online Reinforcement Learning with Applications in Business and Healthcare

Music City Center 

Reinforcement learning (RL) has achieved remarkable success in engineering-focused domains. However, its application to high-stakes, human-centered fields such as business and healthcare remains challenging due to unique barriers: significant heterogeneity among individuals, the continuity of state and action spaces, and heightened demands for interpretability and online algorithms. To address these challenges, we propose a personalized reinforcement learning framework that accounts for both individual heterogeneity and shared patterns across human subjects regarding their state-transition and reward-generating mechanisms through a novel personalized kernel embedding approach. Building on our model, we develop an efficient online RL algorithm. We demonstrate the efficacy of our approach through a rigorous regret analysis and showcase its interpretability through practical case studies.

Data-driven decision-making, Reinforcement Learning, Personalization, Interpretable Algorithms