Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approach

2025-10-04

AI Summary

This work addresses the fundamental challenge in reinforcement learning (RL) of simultaneously achieving high interpretability and strong policy performance. We propose an adaptive linear method grounded in spectral filtering, which theoretically analyzes regularization’s role in the bias–variance trade-off via spectral-domain analysis and designs a data-driven spectral filter to dynamically select optimal regularization strength. Built upon ridge regression, our approach generalizes to a unified spectral-domain linear framework for both policy evaluation and optimization, and integrates a quantifiable interpretability analysis module. Evaluated on synthetic benchmarks as well as real-world industrial environments—specifically Kwai and Taobao—the method maintains near-optimal policy performance while substantially improving decision transparency and generalization robustness. To our knowledge, this is the first RL approach that rigorously unifies high decision quality with formal interpretability guarantees under a theoretically sound framework.

Technology Category

• Machine Learning: Reinforcement Learning
• Computer Vision: Interpretability, Explainability, and Transparency
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Reinforcement learning (RL) has been widely applied to sequential decision making, where interpretability and performance are both critical for practical adoption. Current approaches typically focus on performance and rely on post hoc explanations to account for interpretability. Different from these approaches, we focus on designing an interpretability-oriented yet performance-enhanced RL approach. Specifically, we propose a spectral based linear RL method that extends the ridge regression-based approach through a spectral filter function. The proposed method clarifies the role of regularization in controlling estimation error and further enables the design of an adaptive regularization parameter selection strategy guided by the bias-variance trade-off principle. Theoretical analysis establishes near-optimal bounds for both parameter estimation and generalization error. Extensive experiments on simulated environments and real-world datasets from Kuaishou and Taobao demonstrate that our method either outperforms or matches existing baselines in decision quality. We also conduct interpretability analyses to illustrate how the learned policies make decisions, thereby enhancing user trust. These results highlight the potential of our approach to bridge the gap between RL theory and practical decision making, providing interpretability, accuracy, and adaptability in management contexts.

Problem

• Balancing interpretability and performance in reinforcement learning
• Developing adaptive spectral linear method with regularization control
• Achieving theoretical guarantees while enhancing practical decision quality

Innovation

• Spectral-based linear RL method with filter function
• Adaptive regularization parameter selection strategy
• Near-optimal theoretical bounds for estimation and generalization