5 citations · 5 across the 9 of their papers we have counts for
3 papers · 1 filter
Explaining Reinforcement Learning Agents via Inductive Logic Programming
Celeste Veronese, Edoardo Zorzi, Daniele Meli +1
Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centr…
Sample-efficient Neuro-symbolic Proximal Policy Optimization
Simone Murari, Celeste Veronese, Daniele Meli
Deep Reinforcement Learning (DRL) algorithms often require a large amount of data and struggle in sparse-reward domains with long planning horizons and multiple sub-goals. In this…
Sample-Efficient Neurosymbolic Deep Reinforcement Learning
Celeste Veronese, Alessandro Farinelli, Daniele Meli
Reinforcement Learning (RL) is a well-established framework for sequential decision-making in complex environments. However, state-of-the-art Deep RL (DRL) algorithms typically req…