arXiv AI June 9, 2026 By Ashkan Ansarifard (Sapienza University of Rome), Matteo Mancanelli (Sapienza University of Rome), Elena Umili (Sapienza University of Rome), Fabio Patrizi (Sapienza University of Rome)

Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies

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arXiv:2606. 08312v1 Announce Type: new Abstract: In this work we study offline reinforcement learning (RL) under temporally extended task constraints expressed in Linear Temporal Logic over finite traces (LTLf).

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

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Time to Reason: Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics

arXiv:2608. 16443v1 Announce Type: new Abstract: Neurosymbolic (NeSy) Artificial Intelligence aims to integrate Deep Learning (DL) architectures with symbolic reasoning.

By Riccardo Andreoni, Andrei Buliga, Alessandro Daniele, Paolo Felli, Chiara Ghidini, Marco Montali, Massimiliano Ronzani

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2d ago

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

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2d ago

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

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arXiv Machine Learning

1d ago

Temporal Logic Guided Universal Task Representations for Reinforcement Learning

arXiv:2608. 15509v1 Announce Type: cross Abstract: Task guided agents demonstrate strong performance in a wide range of complex tasks.

By Hao Zhang, Zhangli Zhou, Zhen Kan

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arXiv Machine Learning

Jul 28

Constrained Reinforcement Learning Using Successor Representations

arXiv:2607. 24057v1 Announce Type: new Abstract: Real-world Reinforcement Learning depends on the ability to formulate safety constraints into a policy.

By Michael Girstl, Alexander Mattick, Christopher Mutschler

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arXiv AI

5d ago

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

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