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October 10, 2025Open Access

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

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Authors

QYQianxin YiSLShao-Bo LinJFJun Fan

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Overview

This method improves decision quality and ensures interpretability in reinforcement learning, suggesting a novel approach to balancing these factors.

Key Points

  • The proposed spectral based linear reinforcement learning method enhances interpretability and performance.
  • Experiments demonstrate that the method either outperforms or matches baselines in decision quality.
  • Theoretical analysis shows near-optimal bounds for parameter estimation and generalization error.
  • Interpretability analyses illustrate how learned policies enhance user trust in decision-making.

Cite This Study

Yi et al. (2025) studied this question.

synapsesocial.com/papers/68e865117ef2f04ca37e4dc9https://doi.org/10.48550/arxiv.2510.03722
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  4. 4A Study of Explainability Inquiry Based on Reinforcement Learning2025
  5. 5Balancing the Scales: Reinforcement Learning for Fair Classification2024 · 1 citations