arXiv AI September 11, 2026 By Yash Paliwal, Rajarshi Roy, Jean-Rapha\"el Gaglione, Nasim Baharisangari, Daniel Neider, Xiaoming Duan, Ufuk Topcu, Zhe Xu

Reinforcement Learning with Temporal-Logic-Based Causal Diagrams

Read the original on arXiv AI →

The paper introduces Temporal-Logic-based Causal Diagrams (TL-CDs) for reinforcement learning tasks that involve temporally extended goals. TL-CDs encode causal relationships among environmental properties, complementing deterministic finite automata that model rewards. By leveraging TL-CDs, the authors design an RL algorithm that can predict expected rewards early, leading to significantly reduced exploration and faster convergence to optimal policies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Sep 10

Temporal-Causal Inference for Reinforcement Learning via Automata Learning

arXiv:2609.07461v1 Announce Type: new Abstract: We consider reinforcement learning in environments with dynamics that undergo an irreversible phase transition governed by a hidden temporal pattern. T...

By Jan Corazza, Daniil Kaminskyi, Simon Lutz, Patrick Nossol, Hadi Partovi Aria, Zhe Xu, Daniel Neider

agents reinforcement-learning

arXiv AI

Aug 17

Reward Machines for Signal Temporal Logic

arXiv:2608. 13625v1 Announce Type: new Abstract: Signal temporal logic (STL) provides a formal language for specifying real-time properties of real-valued observations, along with a quantitative robustness score for monitoring satisfaction.

By Alper Kamil Bozkurt, Shangtong Zhang, Yuichi Motai

agents reinforcement-learning

arXiv AI

Jul 21

When to Plan: Learning to Select Between Reactive Control and Deliberative Planning

arXiv:2607. 16421v1 Announce Type: new Abstract: It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning.

By Adam Labiosa, Josiah P. Hanna

agents reinforcement-learning

arXiv AI

Aug 14

Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning

arXiv:2505. 13372v2 Announce Type: replace Abstract: Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given.

By Irene Brugnara, Alessandro Valentini, Andrea Micheli

reinforcement-learning

arXiv Machine Learning

3d ago

Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specifications

The paper introduces an end‑to‑end model‑based reinforcement learning algorithm that synthesises policies satisfying Linear Temporal Logic (LTL) specifications in unknown environments. It synchronises a Limit‑Deterministic Büchi Automaton (LDBA) with a Bayes‑Adaptive Markov Decision Process (BAMDP) and proposes a novel Bayes‑Adaptive Monte‑Carlo Planning (BAMCP) method for approximate Bayes‑optimal strategy synthesis. Experiments on finite and infinite‑horizon tasks show improved property satisfaction and sample efficiency compared to model‑free baselines, and ablation studies confirm the advantage of the new BAMCP over classical variants, including reduced task violations in cautious RL settings.

By Jonathan Hau, Alessandro Abate

reinforcement-learning benchmarks safety

arXiv AI

Aug 17

Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions

arXiv:2602. 06746v2 Announce Type: replace Abstract: We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks.

By Alessandro Abate, Giuseppe De Giacomo, Mathias Jackermeier, Jan Kret\'insk\'y, Maximilian Prokop, Christoph Weinhuber

rag agents reinforcement-learning benchmarks

One email a morning, machine-written

Related stories

Temporal-Causal Inference for Reinforcement Learning via Automata Learning

Reward Machines for Signal Temporal Logic

When to Plan: Learning to Select Between Reactive Control and Deliberative Planning

Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning

Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specifications

Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions