1 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
Edoardo Scarpel, Alberto Pettena, Matteo Cederle +3
This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-sharing systems, overcoming the limitations of periodic, system-wide inte…
A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management
Matteo Cederle, Marco Fabris, Gian Antonio Susto
This study introduces a novel multi-objective reinforcement learning (MORL) approach for autonomous intersection management, aiming to balance traffic efficiency and environmental…
VoI-aware Scheduling Schemes for Multi-Agent Formation Control
Federico Chiariotti, Marco Fabris
Formation control allows agents to maintain geometric patterns using local information, but most existing methods assume ideal communication. This paper introduces a goal-oriented…
Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learning
Matteo Cederle, Marco Fabris, Gian Antonio Susto
In the growing field of Shared Micromobility Systems, which holds great potential for shaping urban transportation, fairness-oriented approaches remain largely unexplored. This wor…
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…