Hugging Face Trending Papers June 29, 2026

Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees

Read the original on Hugging Face Trending Papers →

Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur. Probability-raising (PR) causality addresses this by identifying states whose visitation increases the probability of reaching designated states.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI

Jun 30

Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees

arXiv:2606. 29681v1 Announce Type: new Abstract: Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur.

By Ryohei Oura, Georgios Fainekos, Hideki Okamoto, Bardh Hoxha

reinforcement-learning benchmarks

arXiv AI

Jul 31

Property-driven Causal Abstractions for Markov Decision Processes

arXiv:2607. 26787v1 Announce Type: new Abstract: Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and their valuations.

By Jule Schmidt, Maximilian Weininger, Clemens Dubslaff, David Parker, Nils Jansen

reinforcement-learning benchmarks

arXiv Machine Learning

Aug 4

Near-Optimal Reinforcement Learning for Constrained Recurrence Objectives

arXiv:2511. 19849v2 Announce Type: replace-cross Abstract: Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $\omega$-regular and linear temporal logic (LTL) objectives.

By Dominik Wagner, Leon Witzman, Luke Ong

reinforcement-learning safety

arXiv AI

Jun 2

Robust Shielding for Safe Reinforcement Learning

arXiv:2606. 00270v1 Announce Type: new Abstract: Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs).

By Edwin Hamel-De le Court, Thom Badings, Alessandro Abate, Francesco Belardinelli, Francesco Fabiano

agents reinforcement-learning safety

arXiv AI

Jun 11

Planning under Distribution Shifts with Causal POMDPs

arXiv:2602. 23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts.

By Matteo Ceriscioli, Karthika Mohan

reinforcement-learning

arXiv AI

Aug 10

From probability to causality in probabilistic logic programming

arXiv:2608. 07230v1 Announce Type: new Abstract: Probabilistic logic programming is a formalism of statistical relational artificial intelligence that supports causal queries, including interventions from outside the system.

By Zora Wurm, Kilian R\"uckschlo{\ss}, Felix Weitk\"amper

Related stories

Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees

Property-driven Causal Abstractions for Markov Decision Processes

Near-Optimal Reinforcement Learning for Constrained Recurrence Objectives

Robust Shielding for Safe Reinforcement Learning

Planning under Distribution Shifts with Causal POMDPs

From probability to causality in probabilistic logic programming