AI RESEARCH
A Measure-Theoretic Finite-Sample Theory for Adaptive-Data Fitted Q-Iteration
arXiv CS.LG
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ArXi:2605.05791v1 Announce Type: new While reinforcement learning (RL) promises to revolutionize the control of complex nonlinear robotic systems, a profound gap persists between the heuristic success of model-free off-policy deep RL and the underlying theory, which remains largely confined to tabular or linearizable settings.