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cs.AI2026
Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions
Alessandro Abate, Giuseppe De Giacomo, Mathias Jackermeier +4
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. We consi…
cs.AI2025
Zero-Shot Instruction Following in RL via Structured LTL Representations
Mattia Giuri, Mathias Jackermeier, Alessandro Abate
Linear temporal logic (LTL) is a compelling framework for specifying complex, structured tasks for reinforcement learning (RL) agents. Recent work has shown that interpreting LTL i…
cs.AI2025
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL
Mathias Jackermeier, Alessandro Abate
Linear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in multi-task reinforcement learning (RL). However,…