Hugging Face Trending Papers June 23, 2026

Solving Markov Decision Processes with Future Information via MPC

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Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based planning. However, despite these strengths, an MPC scheme typically does not yield optimal policies for sequential decision-making problems formulated as Markov Decision Processes (MDPs).

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arXiv Machine Learning

Jun 25

Solving Markov Decision Processes with Future Information via MPC

arXiv:2606. 24991v1 Announce Type: cross Abstract: Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based planning.

By Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt, Sebastien Gros

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Jun 6

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By Lidor Erez, Shahaf S. Shperberg, Ayal Taitler

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Jun 16

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By Youngjae Min, Jovin D'sa, Faizan M. Tariq, David Isele, Navid Azizan, Sangjae Bae

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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.

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reinforcement-learning

arXiv AI

Aug 7

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arXiv:2605. 04568v3 Announce Type: replace-cross Abstract: State-of-the-art model-based Reinforcement Learning (RL) approaches either use gradient-free, population-based methods for planning, learned policy networks, or a combination of policy networks and planning.

By Jonathan Spieler, Sven Behnke

reinforcement-learning benchmarks

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

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