arXiv Machine Learning June 15, 2026 By Tianhao Wu, Matthew Zurek, Weina Wang, Qiaomin Xie

Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

Read the original on arXiv Machine Learning →

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.

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arXiv Machine Learning

Aug 18

Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs

arXiv:2509. 16586v2 Announce Type: replace Abstract: Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDPs) under the generative model.

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arXiv Machine Learning

Jun 16

Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process

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.

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Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Bandits

arXiv:2511.08097v2 Announce Type: replace-cross Abstract: We consider a general infinite horizon Heterogeneous Restless multi-armed Bandit (RMAB). Heterogeneity is a fundamental problem for many real...

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reinforcement-learning

arXiv Machine Learning

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Policy Gradient for Continuous-Time Robust Markov Decision Processes

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Achieving an $O(1/N)$ Optimality Gap in Average-Reward Weakly-Coupled MDPs

arXiv:2609.38132v1 Announce Type: new Abstract: We study average-reward weakly-coupled Markov decision processes (WCMDPs), where a WCMDP consists of $N$ smaller MDPs, called arms, that share multiple...

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