arXiv Machine Learning July 14, 2026 By Tomoki Yamagami, Mikio Hasegawa, Takatomo Mihana, Ryoichi Horisaki, Atsushi Uchida

Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems

Read the original on arXiv Machine Learning →

arXiv:2603. 05559v2 Announce Type: replace Abstract: Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optical signal as the driving source for sequential decisions.

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

arXiv AI

Jun 4

Constraint-Enhanced Physical Search through Correlation Matching

arXiv:2606. 03554v1 Announce Type: cross Abstract: Physical systems do not merely add noise to search processes; they impose constraints that generate structured correlations.

By Song-Ju Kim

reinforcement-learning

arXiv Machine Learning

Jun 9

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

arXiv:2606. 09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time.

By Deqi Zheng, Xiaoyang Xu, Yuhong Yang

reinforcement-learning benchmarks

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

Jul 21

Periodic Bootstrap Thompson Sampling For Periodically Non-Stationary Bandit Problems

arXiv:2607. 16986v1 Announce Type: new Abstract: This paper introduces Periodic Bootstrap Thompson Sampling (PBTS), an innovative extension of the classic Thompson Sampling (TS) algorithm tailored for bandit problems with periodic non-stationarity.

By Boning Shao

rag reinforcement-learning

arXiv Machine Learning

Jun 5

Exploration via linearly perturbed loss minimisation

arXiv:2311. 07565v3 Announce Type: replace Abstract: We introduce exploration via linear loss perturbations (EVILL), a randomised exploration method for structured stochastic bandit problems that works by solving for the minimiser of a linearly perturbed regularised negative log-likelihood function.

By David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesv\'ari

reinforcement-learning

arXiv Machine Learning

Jun 30

Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting

arXiv:2409. 05980v2 Announce Type: replace-cross Abstract: Rested and Restless Bandits are two well-known bandit settings that are useful to model real-world sequential decision-making problems in which the expected reward of an arm evolves over time due to the actions we perform or due to the nature.

By Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli

reinforcement-learning

Related stories

Constraint-Enhanced Physical Search through Correlation Matching

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

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

Periodic Bootstrap Thompson Sampling For Periodically Non-Stationary Bandit Problems

Exploration via linearly perturbed loss minimisation

Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting