5 papers
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…
Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time
Celeste Veronese, Daniele Meli, Alessandro Farinelli
This paper proposes an integration of temporal logical reasoning and Partially Observable Markov Decision Processes (POMDPs) to achieve interpretable decision-making under uncertai…
Online inductive learning from answer sets for efficient reinforcement learning exploration
Celeste Veronese, Daniele Meli, Alessandro Farinelli
This paper presents a novel approach combining inductive logic programming with reinforcement learning to improve training performance and explainability. We exploit inductive lear…