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.
Download my CV.
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
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL) using TorchRL. BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.
We propose TorchRL, a generalistic control library for PyTorch that provides well-integrated, yet standalone components. With a versatile and robust primitive design, TorchRL facilitates streamlined algorithm development across the many branches of Reinforcement Learning (RL) and control. We introduce a new PyTorch primitive, TensorDict, as a flexible data carrier that empowers the integration of the library’s components while preserving their modularity. TorchRL fosters long-term support and is publicly available on GitHub for greater reproducibility and collaboration within the research community.
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