arXiv Machine Learning June 11, 2026 By Tommaso Giorgi, Pierriccardo Olivieri, Keyue Jiang, Laura Toni, Matteo Papini

Impact of Connectivity on Laplacian Representations in Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2603. 08558v3 Announce Type: replace Abstract: Learning compact state representations in Markov Decision Processes (MDPs) has proven crucial for addressing the curse of dimensionality in large-scale reinforcement learning (RL) problems.

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

reinforcement-learning

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Learning The Minimum Action Distance

arXiv:2506. 09276v4 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent.

By Lorenzo Steccanella, Joshua B. Evans, \"Ozg\"ur \c{S}im\c{s}ek, Anders Jonsson

rag agents reinforcement-learning

arXiv Machine Learning

Jul 22

Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces

arXiv:2607. 18554v1 Announce Type: cross Abstract: We develop the Continuous Distributed Coupled Policy Gradient (CDCPG) algorithm for cooperative reinforcement learning in networked Markov decision processes with continuous state and action spaces.

By Dongming Wang, Pengcheng Dai, Wenwu Yu, Wei Ren

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 3

Laplacian Representations for Decision-Time Planning

arXiv:2602. 05031v2 Announce Type: replace Abstract: Planning with a learned model remains a key challenge in model-based reinforcement learning (RL).

By Dikshant Shehmar, Matthew Schlegel, Matthew E. Taylor, Marlos C. Machado

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jul 16

Factorized Spectral Representations for Reinforcement Learning

arXiv:2607. 13498v1 Announce Type: new Abstract: Learning a compact model of the world from interaction data is central to sample-efficient deep reinforcement learning.

By Junyi Wu, Dan Li

reinforcement-learning robotics

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 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

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