14 citations · 25 across the 16 of their papers we have counts for
8 papers · 1 filter
Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
Luca Marzari, Enrico Marchesini
History-dependent policies induced by recurrent neural networks (RNNs) rely on latent hidden state dynamics, making verification in partially observable reinforcement learning (RL)…
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…
Improving Policy Optimization via -Retrain
Luca Marzari, Priya L. Donti, Changliu Liu +1
We present -retrain, an exploration strategy encouraging a behavioral preference while optimizing policies with monotonic improvement guarantees. To this end, we intro…
Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing
Luca Marzari, Gabriele Roncolato, Alessandro Farinelli
Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in many scenarios, ranging from pattern recognition to complex robotic problems. However, their…
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…
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…