5 papers · 1 filter
Analyzing Adversarial Inputs in Deep Reinforcement Learning
Davide Corsi, Guy Amir, Guy Katz +1
In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However,…
Seldonian Reinforcement Learning for Ad Hoc Teamwork
Edoardo Zorzi, Alberto Castellini, Leonidas Bakopoulos +2
Most offline RL algorithms return optimal policies but do not provide statistical guarantees on desirable behaviors. This could generate reliability issues in safety-critical appli…
Sentinel: Multi-Patch Transformer with Temporal and Channel Attention for Time Series Forecasting
Davide Villaboni, Alberto Castellini, Ivan Luciano Danesi +1
Transformer-based time series forecasting has recently gained strong interest due to the ability of transformers to model sequential data. Most of the state-of-the-art architecture…
Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
Luca Marzari, Francesco Leofante, Ferdinando Cicalese +1
We study the problem of assessing the robustness of counterfactual explanations for deep learning models. We focus on altering model parameters an…
Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
Davide Corsi, Davide Camponogara, Alessandro Farinelli
An exciting and promising frontier for Deep Reinforcement Learning (DRL) is its application to real-world robotic systems. While modern DRL approaches achieved remarkable successes…