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
arXiv:2602. 14344v2 Announce Type: replace-cross Abstract: We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training.
By Mathias Jackermeier, Mattia Giuri, Jacques Cloete, Alessandro Abate
arXiv:2602. 09761v2 Announce Type: replace-cross Abstract: In this work we address the problem of training a Reinforcement Learning agent to follow multiple temporally-extended instructions expressed in Linear Temporal Logic in sub-symbolic environments.
By Matteo Pannacci, Andrea Fanti, Elena Umili, Roberto Capobianco
arXiv:2609.38065v1 Announce Type: cross
Abstract: Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides...
By Mathias Jackermeier, Jacques Cloete, Alessandro Abate
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)
arXiv:2608. 03502v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents.
By Christophe D. Hounwanou, John Emeka Eze, Ya\'e Ulrich Gaba