VMAS: A Vectorized Multi-agent Simulator for Collective Robot Learning. - Matteo Bettini; Ryan Kortvelesy; Jan Blumenkamp; Amanda Prorok (DARS, 2022)

On the properties of path additions for traffic routing. - Matteo Bettini; Amanda Prorok (CoRR, 2022)

Heterogeneous Multi-Robot Reinforcement Learning. - Matteo Bettini; Ajay Shankar; Amanda Prorok (AAMAS, 2023)

POPGym: Benchmarking Partially Observable Reinforcement Learning. - Steven D. Morad; Ryan Kortvelesy; Matteo Bettini; Stephan Liwicki; Amanda Prorok (ICLR, 2023)

System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning. - Matteo Bettini; Ajay Shankar; Amanda Prorok (CoRR, 2023)

TorchRL: A data-driven decision-making library for PyTorch. - Albert Bou; Matteo Bettini; Sebastian Dittert; Vikash Kumar; Shagun Sodhani; Xiaomeng Yang; Gianni De Fabritiis; Vincent Moens (ICLR, 2024)

Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning. - Matteo Bettini; Ryan Kortvelesy; Amanda Prorok (ICML, 2024)

The Cambridge RoboMaster: An Agile Multi-Robot Research Platform. - Jan Blumenkamp; Ajay Shankar; Matteo Bettini; Joshua Bird; Amanda Prorok (CoRR, 2024)

Neural diversity is key to collective artificial learning. - Matteo Bettini; Ryan Kortvelesy; Amanda Prorok (CoRR, 2024)

BenchMARL: Benchmarking Multi-Agent Reinforcement Learning. - Matteo Bettini; Amanda Prorok; Vincent Moens (J. Mach. Learn. Res., 2024)

When Is Diversity Rewarded in Cooperative Multi-Agent Learning? - Michael Amir; Matteo Bettini; Amanda Prorok (CoRR, 2025)