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

THE PROBLEM

This paper focuses on reinforcement learningImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards.. The paper addresses the practical problem that RLImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. agents trained on one robotic taskCore ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. often fail on similar tasks. The authors use SHAP (Shapley Additive exPlanations) to analyze how different RLImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. algorithms and hyperparameter choices contribute to generalizationModern Robot LearningGeneralizationThe robot’s ability to work in new situations it has not seen before. gaps. They establish theoretical connections between Shapley values and generalizability, then propose a framework to guide configuration selection based on these insights. Results show consistent patterns that can improve transfer across robotic environments. Read the paper by tracking the taskCore ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. definition, the robotCore ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or data assumptions, and the evidence that supports the claimed improvement.

HOW IT WORKS

1

Task framing

The paper frames the work as reinforcement learningImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards.. Start here because it defines what success means and which assumptions the rest of the method inherits.

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2

Core method

This paper gives you a systematic way to pick RLImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. algorithm configurations that generalize better across different robotic tasks, using SHAP to decompose which hyperparameters actually matter. Instead of trial-and-error tuning, you get explainable guidance on what configuration choices reduce the gap between trainingRobot LearningTrainingThe process of fitting a model using data or experience. and deploymentSimulation & Sim-to-RealDeploymentPutting the trained system on a real robot.. When reading the method section, identify the inputs, the learned or engineered representation, and the actionCore ConceptsActionA command the robot sends to its motors, controller, or low-level system. or prediction produced by the system.

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Figure 1: Our work aims to analyze and guide cross-environment generalization of RL models through bidirectional transfer experiments: MuJoCo \leftrightarrow PyBullet.

Figure 2: SHAP-based configuration framework: (1) sample algorithm and hyperparameter configurations, (2) train RL models in source environment, (3) evaluate generalization gap across environments, (4) train surrogate SHAP explainer, and (5) analyze impact patterns and select optimal configurations.

Figure 3: Main impact patterns across all algorithms and hyperparameters, lower (leftward) SHAP values indicate better generalizability.

Figure 6: SHAP dependence of learning rate and gamma across tasks and environments.

3

Data and supervision

For robotics work, the data story is part of the method: check whether the system depends on teleoperationImitation & Reinforcement LearningTeleoperation (teleop)A human remotely controlling the robot, often to collect demonstrations., simulationSimulation & Sim-to-RealSimulationA virtual environment where robots can be trained or tested., internet video, human labels, or robotCore ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. rollouts.

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4

Evaluation evidence

The paper should be judged through its evaluationSimulation & Sim-to-RealEvaluationMeasuring how well a robot system performs. protocol: what data is used, what robotCore ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or simulator is tested, and which baselineEvaluation & ResearchBaselineA reference method used for comparison. comparisons support the claim. Look for the gap between the headline result and the deploymentSimulation & Sim-to-RealDeploymentPutting the trained system on a real robot. setting you would actually care about.

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FIGURES

Figure 4: Feature interactions between hyperparameters in all four algorithms, where darker blue indicates stronger beneficial interactions.

Figure 5: Feature interaction between learning rate and gamma in DDPG algorithm.

Figure 7: Best and worst configurations identified by SHAP analysis.

KEY RESULTS

WHY DEVELOPERS SHOULD CARE

LIMITATIONS

The main limitation to check is whether the claimed behavior holds outside the paper's reported setup. That means testing across different robotCore ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. embodiments, scenes, objects, and data distributions.

WHAT COMES NEXT

The practical next step is independent reproduction with clear baselines, ablations, and stress tests. For a developer, the useful follow-up is to map the paper's reinforcement learningImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. assumptions onto a concrete robotCore ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. stack, then test the smallest version of the method that could run end to end.

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