52 citations · 112 across the 27 of their papers we have counts for
10 papers · 1 filter
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…
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…
An attention model for the formation of collectives in real-world domains
Adrià Fenoy, Filippo Bistaffa, Alessandro Farinelli
We consider the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e.g., shared mobility, cooperative learning). We pr…
Curriculum Learning for Safe Mapless Navigation
Luca Marzari, Davide Corsi, Enrico Marchesini +1
This work investigates the effects of Curriculum Learning (CL)-based approaches on the agent's performance. In particular, we focus on the safety aspect of robotic mapless navigati…
Rule-based Shielding for Partially Observable Monte-Carlo Planning
Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The…
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach
Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The…
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification
Davide Corsi, Enrico Marchesini, Alessandro Farinelli
Groundbreaking successes have been achieved by Deep Reinforcement Learning (DRL) in solving practical decision-making problems. Robotics, in particular, can involve high-cost hardw…