arXiv Machine Learning June 2, 2026 By Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Topology-Aware State Abstraction with Tangle Cores for Markov Decision Processes

Read the original on arXiv Machine Learning →

arXiv:2606. 00427v1 Announce Type: new Abstract: State abstraction in reinforcement learning is usually formulated as a partition of states based on reward and transition similarity.

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

reinforcement-learning

arXiv Machine Learning

Jun 17

Performance-Driven Environment Abstraction with Multi-Timescale Learning

arXiv:2606. 17377v1 Announce Type: new Abstract: We study performance-driven environment abstraction for decision-making in large Markov decision processes.

By Yue Guan, Dipankar Maity, Panagiotis Tsiotras

reinforcement-learning

arXiv Machine Learning

Jun 11

Impact of Connectivity on Laplacian Representations in Reinforcement 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.

By Tommaso Giorgi, Pierriccardo Olivieri, Keyue Jiang, Laura Toni, Matteo Papini

reinforcement-learning

arXiv Machine Learning

Aug 7

Stochastic Dynamics on Persistence Diagram Space via Reinforcement Learning

arXiv:2608. 06276v1 Announce Type: cross Abstract: Persistence diagrams (PDs) provide stable and interpretable summaries of multiscale topological structure.

By Farzana Nasrin

reinforcement-learning

arXiv AI

Jul 21

Reward-Driven LLM Agent Workflows: Synthesizing POMDP Routing and Self-Correction for Autonomous Decision-Making

arXiv:2607. 17038v1 Announce Type: new Abstract: This paper addresses key technical challenges in current large language model (LLM) agent applications, including long-horizon planning, sparse reward attribution, and dynamic environmental interaction, by designing and optimizing an intelligent agent workflow.

By Amez Amanj Ali, Kuo-Kun Tseng

llms agents reinforcement-learning robotics multimodal benchmarks safety

arXiv Machine Learning

Jul 22

S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning

arXiv:2607. 19232v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution.

By Kshitij Kumar Srivastava, Kshitij Jerath

agents reinforcement-learning benchmarks

arXiv AI

Jun 10

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes

arXiv:2512. 14617v2 Announce Type: replace-cross Abstract: Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties.

By Alessandro Trapasso, Luca Iocchi, Fabio Patrizi

agents reinforcement-learning benchmarks

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Performance-Driven Environment Abstraction with Multi-Timescale Learning

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Stochastic Dynamics on Persistence Diagram Space via Reinforcement Learning

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