5 citations · 5 across the 10 of their papers we have counts for
12 papers
Chess on Ice: Curling Tactical Decision-Making via Backward Induction and Deep Reinforcement Learning
Patrick Oberlin, Matteo Cederle, Aren Karapetyan +3
Curling is often referred to as "Chess on Ice", owing to the tactical complexity of its decision-making process. Yet unlike chess, curling remains largely underexplored from a mach…
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…
Towards Model-Free Learning in Dynamic Population Games: An Application to Karma Economies
Matteo Cederle, Saverio Bolognani, Gian Antonio Susto
Dynamic Population Games (DPGs) provide a tractable framework for modeling strategic interactions in large populations of self-interested agents, and have been successfully applied…
Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
Matteo Cederle, Giacomo Scatto, Gian Antonio Susto
Urban traffic congestion presents a significant challenge for modern cities, which impacts mobility and sustainability. Traditional traffic light control systems often fail to adap…
Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
Riccardo De Monte, Matteo Cederle, Gian Antonio Susto
State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible…
Towards Fair and Efficient allocation of Mobility-on-Demand resources through a Karma Economy
Matteo Cederle, Saverio Bolognani, Gian Antonio Susto
Mobility-on-demand systems like ride-hailing have transformed urban transportation, but they have also exacerbated socio-economic inequalities in access to these services, also due…
Explainable Anomaly Detection for Electric Vehicles Charging Stations
Matteo Cederle, Andrea Mazzucco, Andrea Demartini +4
Electric vehicles (EV) charging stations are one of the critical infrastructures needed to support the transition to renewable-energy-based mobility, but ensuring their reliability…
Towards Explainable Anomaly Detection in Shared Mobility Systems
Elnur Isgandarov, Matteo Cederle, Federico Chiariotti +1
Shared mobility systems, such as bike-sharing networks, play a crucial role in urban transportation. Identifying anomalies in these systems is essential for optimizing operations,…
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…
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 Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning
Matteo Cederle, Marco Fabris, Gian Antonio Susto
Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised se…
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…