arXiv:2606. 14095v1 Announce Type: new Abstract: We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model.
By Tianhao Wu, Matthew Zurek, Weina Wang, Qiaomin Xie
arXiv:2609.37660v1 Announce Type: new
Abstract: We study nonpreemptive contextual queueing bandits in a single-server system. Each job is represented by a $d$-dimensional context vector; in each roun...
By Wansoo Choi, Seoungbin Bae, Dabeen Lee
arXiv:2609.36486v1 Announce Type: new
Abstract: We study an unknown-transition finite-horizon Markov decision process (MDP) with a finite collection of known reward functions $\{r^1, r^2, \ldots, r^M...
By Zijun Chen, Zihan Zhang
arXiv:2302.07477v4 Announce Type: replace
Abstract: We study the optimal sample complexity of tabular reinforcement learning for infinite-horizon discounted Markov decision processes. The unrestricte...
By Shengbo Wang, Jose Blanchet, Peter Glynn
arXiv:2606. 16729v1 Announce Type: new Abstract: While there is an extensive body of work characterizing the sample complexity of discounted cumulative-reward MDPs, finite sample analyses for average-reward MDPs have been limited, and most existing works rely on restrictive assumptions such as ergodicity or access to a generative model.
By Jongmin Lee, Ernest K. Ryu, Vaneet Aggarwal
arXiv:2607. 17201v1 Announce Type: cross Abstract: In this work we study the Best Policy Identification (BPI) problem in online, tabular Reinforcement Learning.
By Joseph Lazzaro, Alessio Russo, Aldo Pacchiano