arXiv Machine Learning September 4, 2026 By Jiashuo Jiang, Yinyu Ye, Yiming Zong

Adaptive Resolving Methods for Markov Decision Processes with Function Approximations

Read the original on arXiv Machine Learning →

arXiv:2505. 12037v2 Announce Type: replace Abstract: Learning the optimal policy for Markov decision process problems (MDPs) from samples is a fundamental problem in online and data-driven decision-making.

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reinforcement-learning

arXiv Machine Learning

Aug 10

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions

arXiv:2608. 06545v1 Announce Type: new Abstract: Distributionally robust Markov decision processes provide a principled framework for sequential decision making under model uncertainty.

By Yuepeng Yang, Yuxin Chen, Yuejie Chi

reinforcement-learning safety

arXiv Machine Learning

Jun 15

Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

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

reinforcement-learning safety

arXiv Machine Learning

Jun 25

Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.

By Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet

agents reinforcement-learning

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.

By Yukuan Wei, Xudong Li, Lin F. Yang

reinforcement-learning safety

arXiv Machine Learning

Jun 3

Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning

arXiv:2603. 03480v2 Announce Type: replace Abstract: We study reinforcement learning with delayed state observation, where the agent observes the current state after some random number of time steps.

By Harin Lee, Kevin Jamieson

agents reinforcement-learning

arXiv Machine Learning

Jul 21

Non-Asymptotic Best Policy Identification Guarantees in Online Reinforcement Learning

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

agents reinforcement-learning

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