Daniele Meli

Explaining Reinforcement Learning Agents via Inductive Logic Programming

Celeste Veronese, Edoardo Zorzi, Daniele Meli, Alessandro Farinelli

Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios. However, it is mostly based on user studies, thus targeting the needs of a specific au…Read more