5 citations · 5 across the 9 of their papers we have counts for
9 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…
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
Amirhossein Zhalehmehrabi, Daniele Meli, Francesco Dal Santo +2
Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances an…
Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments
Lorenzo Bonanni, Daniele Meli, Alberto Castellini +1
Online motion planning is a challenging problem for intelligent robots moving in dense environments with dynamic obstacles, e.g., crowds. In this work, we propose a novel approach…
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…
Planning and Inverse Kinematics of Hyper-Redundant Manipulators with VO-FABRIK
Cristian Morasso, Daniele Meli, Yann Divet +2
Hyper-redundant Robotic Manipulators (HRMs) offer great dexterity and flexibility of operation, but solving Inverse Kinematics (IK) is challenging. In this work, we introduce VO-FA…
Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach
Daniele Meli, Alberto Castellini, Alessandro Farinelli
Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distr…