[2603.27971] Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learning

Computer Science > Machine Learning arXiv:2603.27971 (cs) [Submitted on 30 Mar 2026] Title:Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learning Authors:Bodla Krishna Vamshi, Haizhao Yang View a PDF of the paper titled Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learning, by Bodla Krishna Vamshi and 1 other authors View PDF HTML (experimental) Abstract:Recent years have witnessed the widespread adoption of reinforcement learning (RL), from solving real-time games to fine-tuning large language models using human preference data significantly improving alignment with user expectations. However, as model complexity grows exponentially, the interpretability of these systems becomes increasingly challenging. While numerous explainability methods have been developed for computer vision and natural language processing to elucidate both local and global reasoning patterns, their application to RL remains limited. Direct extensions of these methods often struggle to maintain the delicate balance between interpretability and performance within RL settings. Prototype-Wrapper Networks (PW-Nets) have recently shown promise in bridging this gap by enhancing explainability in RL domains without sacrificing the efficiency of the original black-box models. However, these methods typically require manually defined reference prototypes, which often necessitate expert domain knowledge. In this work, we propose a method that removes this...