By analyzing which algorithm settings and hyperparameters contribute most to generalization gaps in robot learning, you can make smarter configuration choices that work across different tasks—rather than tuning blindly for each new environment.
This paper uses SHAP (a technique for explaining AI decisions) to understand which RL algorithm choices and hyperparameters matter most for robot learning across different environments. The authors show that certain configurations consistently help models generalize better, and they use these insights to automatically select better configurations.