Matteo Bettini

Matteo Bettini

PhD Candidate

Prorok Lab

University of Cambridge

Hello! 👋

I am Matteo, a PhD student in the Prorok Lab at the University of Cambridge.

With my supervisor, Amanda Prorok, I study resilience and heterogeneity in multi-agent and multi-robot systems. For my research, I employ techniques from the fields of Multi-Agent Reinforcement Learning and Graph Neural Networks.

Prior to my PhD, I investigated the problem of transport network design for multi-agent routing.

Interests
  • Multi-Robot Systems
  • Heterogeneous Multi-Agent Learning and Coordination
  • Reinforcement Learning
  • Graph Neural Networks
Education

  • PhD in Computer Science, Present

    University of Cambridge

  • MPhil in Advanced Computer Science, 2021

    University of Cambridge

  • BEng in Computer Engineering, 2020

    Politecnico di Milano

Publications

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All Multi-Agent Reinforcement Learning Transport Network Design
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
In this paper, we introduce the Vectorized Multi-Agent Simulator (VMAS). VMAS is an open-source framework designed for efficient MARL benchmarking. It comprises a vectorized 2D physics engine written in PyTorch and a set of twelve challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface. We demonstrate how vectorization enables parallel simulation on accelerated hardware without added complexity.
2022 In Distributed Autonomous Robotic Systems (DARS) Multi-Agent Reinforcement Learning
PDF Cite Code Video arXiv Talk
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
On the Properties of Path Additions for Traffic Routing
In this paper, we investigate the impact of path additions to transport networks with optimised traffic routing. In particular, we study the behaviour of total travel time, and consider both self-interested routing paradigms, such as User Equilibrium (UE) routing, as well as cooperative paradigms, such as classic Multi-Commodity (MC) network flow and System Optimal (SO) routing. This work aims to provide an analysis and categorization of the properties of objective functions for transport network design, with the purpose of informing algorithm (and also network) designers. Among our results, we prove, via counterexample, that total travel time, under both cooperative and self-interested routing, is not supermodular with respect to path additions.
2022 In IEEE International Conference on Intelligent Transportation Systems (ITSC) Workshop on Co-Design and Coordination of Future Mobility Systems Transport Network Design
PDF Cite Code arXiv
On the Properties of Path Additions for Traffic Routing

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