SHAP分析によるロボティクス強化学習の一般化性能向上：アルゴリズムとハイパーパラメータの影響解明
Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
強化学習の性能がアルゴリズムやハイパーパラメータに敏感である問題に対し、SHAP値を用いて各設定の一般化ギャップへの寄与を定量分解し、その知見に基づく設定選択で一般化性能を向上させる枠組みを提案した。
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著者: Lingxiao Kong, Cong Yang, Oya Deniz Beyan, Zeyd Boukhers
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Despite significant advances in Reinforcement Learning (RL), model performance remains highly sensitive to algorithm and hyperparameter configurations, while generalization gaps across environments complicate real-world deployment. Although prior work has studied RL generalization, the relative contribution of specific configurations to the generalization gap has not been quantitatively decomposed and systematically leveraged for configuration selection. To address this limitation, we propose an explainable framework that evaluates RL performance across robotic environments using SHapley Additive exPlanations (SHAP) to quantify configuration impacts. We establish a theoretical foundation connecting Shapley values to generalizability, empirically analyze configuration impact patterns, and introduce SHAP-guided configuration selection to enhance generalization. Our results reveal distinct patterns across algorithms and hyperparameters, with consistent configuration impacts across diverse tasks and environments. By applying these insights to configuration selection, we achieve improved RL generalizability and provide actionable guidance for practitioners.
