52 citations · 112 across the 27 of their papers we have counts for
4 papers · 2 filters
Learning Logic Specifications for Soft Policy Guidance in POMCP
Giulio Mazzi, Daniele Meli, Alberto Castellini +1
Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces by com…
Safe Deep Reinforcement Learning by Verifying Task-Level Properties
Enrico Marchesini, Luca Marzari, Alessandro Farinelli +1
Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL). However, the cost is typically encoded as an indicator function due to the difficulty of quantifying…
Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation
Luca Marzari, Enrico Marchesini, Alessandro Farinelli
Safety is essential for deploying Deep Reinforcement Learning (DRL) algorithms in real-world scenarios. Recently, verification approaches have been proposed to allow quantifying th…
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks
Luca Marzari, Davide Corsi, Ferdinando Cicalese +1
Deep Neural Networks are increasingly adopted in critical tasks that require a high level of safety, e.g., autonomous driving. While state-of-the-art verifiers can be employed to c…