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

2026-05-04 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, quick

Summary

A new explainable framework evaluates Reinforcement Learning (RL) performance across robotic environments using SHapley Additive exPlanations (SHAP) to quantify the impact of algorithm and hyperparameter configurations. This approach addresses the challenge of RL model sensitivity and generalization gaps in real-world deployment by systematically decomposing the relative contribution of specific configurations to the generalization gap. The framework establishes a theoretical foundation linking Shapley values to generalizability, analyzes configuration impact patterns empirically, and introduces SHAP-guided configuration selection. Results show distinct and consistent configuration impact patterns across various algorithms, hyperparameters, tasks, and environments, leading to improved RL generalizability and practical guidance for practitioners.

Key takeaway

For research scientists developing RL agents for robotics, understanding how algorithm and hyperparameter choices affect generalization is critical. You should consider integrating SHAP analysis into your RL workflow to quantitatively assess configuration impacts, enabling more informed selection and leading to more robust and generalizable models for diverse robotic tasks.

Key insights

SHAP analysis quantifies RL configuration impact to enhance generalizability across robotic environments.

Principles

• RL performance is sensitive to configurations.
• Shapley values can quantify configuration impact.

Method

The framework uses SHAP to evaluate RL performance, connecting Shapley values to generalizability, analyzing impact patterns, and guiding configuration selection.

In practice

• Use SHAP to identify impactful RL configurations.
• Apply SHAP-guided selection for better generalization.

Topics

• Reinforcement Learning
• Robotics
• Generalizability
• SHAP Analysis
• Hyperparameter Optimization

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.