arXiv AI August 17, 2026 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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Temporal Logic Guided Universal Task Representations for Reinforcement Learning

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