2 papers
cs.LG2026
Open-ended Multi-agent Autocurricula via Visual Inspection of Policies with Multi-modal LLMs
Lorenzo Pantè, Andrea Fanti, Roberto Capobianco
Open-ended curricula in Reinforcement Learning (RL) aim to train generally-capable agents by identifying tasks that facilitate learning increasingly complex skills. A major challen…
cs.LG2026
Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization
Matteo Pannacci, Andrea Fanti, Elena Umili +1
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-symboli…