From the 1 of 11 linked papers with an AI index.
11 papers
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…
Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning
Amirhossein Zhalehmehrabi, Tiziano Tezze, Alberto Castelini +1
We propose a sensor-guided adaptive contrastive learning framework for visual representation learning in PointGoal navigation. During training, privileged LiDAR sensing guides the…
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…
Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification
Luca Marzari, Ferdinando Cicalese, Alessandro Farinelli
We present robabilistically ightened near elaxation-based erturbation nalysis (), a nove…
On the Probabilistic Learnability of Compact Neural Network Preimage Bounds
Luca Marzari, Manuele Bicego, Ferdinando Cicalese +1
Although recent provable methods have been developed to compute preimage bounds for neural networks, their scalability is fundamentally limited by the #P-hardness of the problem. I…
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…