SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

The paper makes environment-specific, empirical claims of 10–100× sample-efficiency gains (e.g., 100× on swing-up; 14.47× on 3D airfoil) and describes practical stability heuristics (resetting/projection), but it does not provide a general theorem establishing a universal 100× guarantee . The candidate model offers a conditional theorem with explicit assumptions and bounds linking model error, planning accuracy, and environment interactions—this is logically sound overall, though a small correction is needed in the reward Lipschitz transfer (a missing factor depending on the policy Lipschitz constant). Net: the paper is empirically correct but theoretically incomplete; the model’s conditional analysis is essentially correct and clarifies when large reductions (e.g., 100×) are attainable.