aiqbench analysis
This research addresses the critical challenge of reinforcement learning generalization in robotics by using SHAP values to quantitatively decompose how algorithmic choices and hyperparameters impact performance gaps across environments. By establishing a theoretical link between Shapley values and generalizability, the authors provide a framework for selecting configurations that consistently improve robustness without relying on trial-and-error tuning. This work offers practitioners actionable insights into optimizing RL systems for diverse real-world tasks, moving beyond black-box performance metrics to explainable configuration selection.
Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
arXiv:2605.02867v3 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2605.02867 [cs.LG]
(or arXiv:2605.02867v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.02867 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 234 words
