arXiv Machine Learning

Bellman Residual Minimization for Control: Geometry, Stationarity, and Convergence

arXiv:2601. 18840v4 Announce Type: replace Abstract: Markov decision problems are most commonly solved via dynamic programming.

arXiv AI

Jun 10

Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets

arXiv:2606. 10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints.

By Yi Chen (Lucy), Rushuai Yang (Lucy), Qiang Chen (Lucy), Dongyan (Lucy), Huo

reinforcement-learning benchmarks

arXiv AI

Jul 14

Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning

arXiv:2506. 07040v4 Announce Type: replace-cross Abstract: We study model-free methods for distributionally robust infinite-horizon average-reward Markov decision processes (MDPs).

By Yang Xu, Swetha Ganesh, Vaneet Aggarwal

reinforcement-learning

arXiv Machine Learning

Jun 25

Low-Complexity Policy Tessellations in Structured Markov Decision Processes

arXiv:2606. 25593v1 Announce Type: new Abstract: We study optimal-policy geometry in structured Markov decision processes.

By Fredy Pokou (CRIStAL)

reinforcement-learning

Hugging Face Trending Papers

Jun 24

Low-Complexity Policy Tessellations in Structured Markov Decision Processes

We study optimal-policy geometry in structured Markov decision processes. While approximate dynamic programming and reinforcement learning typically approximate high-dimensional value functions, we show that optimal policies induce simpler decision tessellations.

reinforcement-learning

arXiv Machine Learning

Jul 1

End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions

arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.

By Zakaria Mhammedi, Alexander Rakhlin, Nneka Okolo

reinforcement-learning

arXiv AI

Jun 18

Robust Regularized Policy Iteration under Transition Uncertainty

arXiv:2603. 09344v3 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift.

By Hongqiang Lin, Zhenghui Fu, Weihao Tang, Pengfei Wang, Yiding Sun, Qixian Huang, Dongxu Zhang

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 9

Mathematical methods of reinforcement learning

arXiv:2607. 06935v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly grounded in tools from probability, optimization, and operator theory.

By Denis Belomestny, Alexander Gasnikov, Egor Gladin, Alexey Naumov, Artemy Rubtsov, Yuri Sapronov, Daniil Tiapkin, Nikita Yudin

reinforcement-learning

arXiv Machine Learning

Jul 17

Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration

arXiv:2310. 07211v2 Announce Type: replace Abstract: Regularization is a cornerstone of modern reinforcement learning.

By Zeyang Li, Chuxiong Hu, Yunan Wang, Guojian Zhan, Jie Li, Yao Lyu, Shengbo Eben Li

reinforcement-learning

arXiv Machine Learning

Jun 2

Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear $q^\pi$-Realizability and Concentrability

arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.

By Volodymyr Tkachuk, Csaba Szepesv\'ari, Xiaoqi Tan

reinforcement-learning

arXiv Machine Learning

Aug 4

Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints

arXiv:2608. 01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints.

By Francesco Cordiano, Kanghui He, Bart De Schutter

reinforcement-learning

arXiv Machine Learning

Jun 9

Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning

arXiv:2605. 26078v3 Announce Type: replace Abstract: Wasserstein policy gradient (WPG) is a policy optimization method for reinforcement learning (RL) that exploits the optimal-transport geometry of action distributions.

By Zhaoyu Zhu, Rui Gao, Shuang Li

diffusion reinforcement-learning safety

arXiv AI

Jul 10

Second-Order Actor-Critic Methods for Discounted MDPs via Policy Hessian Decomposition

arXiv:2605. 14982v2 Announce Type: replace-cross Abstract: We address the discounted reward setting in reinforcement learning (RL).

By Sanjeev Manivannan, Shuban V

reinforcement-learning

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