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.
MPhil in Advanced Computer Science, 2021
University of Cambridge
BEng in Computer Engineering, 2020
Politecnico di Milano
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.