Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters

Problem
This preprint addresses the gap in understanding how specific algorithmic configurations and hyperparameters contribute to the generalization performance of Reinforcement Learning (RL) agents in robotic environments. While previous research has explored RL generalization, it has not quantitatively decomposed the contributions of various configurations to the generalization gap, nor has it systematically utilized this information for configuration selection. This work aims to provide a framework that leverages SHapley Additive exPlanations (SHAP) to analyze and enhance RL generalizability.

Method
The authors propose an explainable framework that employs SHAP to evaluate the performance of RL algorithms across multiple robotic environments. They establish a theoretical connection between Shapley values and generalization, allowing for a systematic analysis of how different configurations affect performance. The methodology includes empirical analysis of configuration impact patterns, where they assess various RL algorithms and hyperparameters. The SHAP-guided configuration selection process is introduced to optimize generalization by selecting configurations that consistently yield better performance across diverse tasks. The paper does not disclose specific training compute requirements but emphasizes the use of SHAP for interpretability in configuration selection.

Results
The framework demonstrates significant improvements in RL generalizability. The authors report that by applying SHAP analysis, they can identify configurations that lead to consistent performance enhancements across different environments. For instance, they achieve a 15% improvement in average return on the OpenAI Gym benchmark compared to standard hyperparameter tuning methods. Additionally, they show that certain configurations lead to a reduction in the generalization gap by up to 20% when evaluated against baseline RL algorithms such as DDPG and PPO. These results indicate that the SHAP-guided approach not only enhances performance but also provides insights into the robustness of configurations across tasks.

Limitations
The authors acknowledge that their approach may be limited by the computational overhead associated with SHAP calculations, particularly in high-dimensional configuration spaces. They also note that while their framework provides actionable insights, it may not generalize to all types of RL algorithms or environments, particularly those with highly stochastic dynamics. Furthermore, the empirical results are based on a limited set of benchmarks, which may not fully capture the diversity of real-world robotic applications. The authors do not address potential biases in SHAP values that could arise from the choice of baseline configurations.

Why it matters
This work has significant implications for the field of RL, particularly in robotics, where deployment in varied environments is critical. By providing a systematic method for configuration selection based on SHAP analysis, the authors offer a pathway to enhance the robustness and generalizability of RL agents. This framework can guide practitioners in selecting hyperparameters that are more likely to yield consistent performance across different tasks, thereby facilitating the transition of RL from research to real-world applications. The insights gained from this study could also inspire further research into explainable AI in RL, potentially leading to more interpretable and reliable models.

Authors: Lingxiao Kong, Cong Yang, Oya Deniz Beyan, Zeyd Boukhers
Source: arXiv:2605.02867
URL: https://arxiv.org/abs/2605.02867v1