5 citations · 11 across the 38 of their papers we have counts for
6 papers · 1 filter
Continual Learning for Behavior-based Driver Identification
Mattia Fanan, Davide Dalle Pezze, Emad Efatinasab +3
Behavior-based Driver Identification is an emerging technology that recognizes drivers based on their unique driving behaviors, offering important applications such as vehicle thef…
AI Olympics challenge with Evolutionary Soft Actor Critic
Marco Calì, Alberto Sinigaglia, Niccolò Turcato +2
In the following report, we describe the solution we propose for the AI Olympics competition held at IROS 2024. Our solution is based on a Model-free Deep Reinforcement Learning ap…
Latent Distillation for Continual Object Detection at the Edge
Francesco Pasti, Marina Ceccon, Davide Dalle Pezze +4
While numerous methods achieving remarkable performance exist in the Object Detection literature, addressing data distribution shifts remains challenging. Continual Learning (CL) o…
Fault Identification Enhancement with Reinforcement Learning (FIERL)
Valentina Zaccaria, Davide Sartor, Simone Del Favero +1
This letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control inpu…
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
Matteo Cederle, Luca Vittorio Piron, Marina Ceccon +4
As Machine Learning grows in popularity across various fields, equity has become a key focus for the AI community. However, fairness-oriented approaches are still underexplored in…
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks
Niccolò Turcato, Alberto Sinigaglia, Alberto Dalla Libera +2
Continuous control Deep Reinforcement Learning (RL) approaches are known to suffer from estimation biases, leading to suboptimal policies. This paper introduces innovative methods…