arXiv Machine Learning July 1, 2026 By Zakaria Mhammedi, Alexander Rakhlin, Nneka Okolo

End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions

Read the original on arXiv Machine Learning →

arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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