arXiv Machine Learning August 3, 2026 By Yanwei Jia, Du Ouyang

Convergence and Regret of the Policy Gradient for Multi-Armed Bandits in Diffusion Environment

Read the original on arXiv Machine Learning →

arXiv:2607. 29593v1 Announce Type: new Abstract: This paper studies the policy gradient update for a multi-arm bandit problem in diffusion environment that is described by a stochastic differential equation (SDE) under the continuous-time reinforcement learning framework by Wang et al.

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

arXiv Machine Learning

Jun 29

Learning in Markovian bandits with non-observable states and constrained decision epochs

arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.

By Thomas Hira, Victor Boone, Urtzi Ayesta, Ina Maria Verloop

reinforcement-learning benchmarks safety

arXiv Machine Learning

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Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

arXiv:2606. 08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this paper.

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arXiv Machine Learning

Jun 9

Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees

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By Deqi Zheng, Xiaoyang Xu, Yuhong Yang

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 5

Multi-Agent Lipschitz Bandits

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agents reinforcement-learning

arXiv Machine Learning

Jun 9

Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret

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By Seoungbin Bae, Dabeen Lee

arXiv Machine Learning

Aug 11

Regret of exploratory policy improvement and $q$-learning

arXiv:2411. 01302v2 Announce Type: replace Abstract: We study the convergence of $q$-learning and related algorithms introduced by Jia and Zhou (J.

By Wenpin Tang, Xun Yu Zhou

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Learning in Markovian bandits with non-observable states and constrained decision epochs

Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

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