From the 1 of 11 linked papers with an AI index.
7 papers · 1 filter
Explaining Reinforcement Learning Agents via Inductive Logic Programming
Celeste Veronese, Edoardo Zorzi, Daniele Meli +1
The paper proposes using Inductive Logic Programming to extract symbolic rules from reinforcement learning policies and introduces objective metrics to quantify how explainable tho…
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…
Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation
Luca Marzari, Isabella Mastroeni, Alessandro Farinelli
Traditional methods for formal verification (FV) of deep neural networks (DNNs) are constrained by a binary encoding of safety properties, where a model is classified as either saf…
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…
Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
Luca Marzari, Francesco Trotti, Enrico Marchesini +1
Achieving safe autonomous navigation systems is critical for deploying robots in dynamic and uncertain real-world environments. In this paper, we propose a hierarchical control fra…
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…