用SHAP分析强化学习配置，提升机器人模型泛化能力
Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
- 用SHAP量化算法与超参数对泛化性能的影响
- 发现不同任务环境下配置影响模式一致
- 指导实践选择更优配置，提升跨环境适应性
尽管强化学习（RL）取得显著进展，模型性能仍高度依赖算法和超参数配置，且在不同环境间存在泛化差距，制约实际部署。现有研究虽关注RL泛化问题，但尚未定量分解特定配置对泛化差距的贡献，并系统用于配置选择。为此，我们提出一种可解释框架，基于SHapley Additive exPlanations（SHAP）评估机器人环境中RL性能，量化配置影响。建立Shapley值与泛化能力的理论联系，实证分析配置影响模式，引入SHAP引导的配置选择以增强泛化。结果揭示算法与超参数存在明显影响模式，且在多种任务与环境间具一致性。利用该洞察进行配置优化，显著提升RL泛化能力，并为从业者提供可操作指导。
原文摘要 · Abstract (English)
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.
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