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

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

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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.

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arXiv AI
Jun 9

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

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).

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)