16 citations · 39 across the 11 of their papers we have counts for
3 papers · 1 filter
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
Matteo Bettini, Amanda Prorok, Vincent Moens
The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a reproducibility crisis. While solutions for standardized reporting have been proposed to address the is…
TorchRL: A data-driven decision-making library for PyTorch
Albert Bou, Matteo Bettini, Sebastian Dittert +5
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams de…
POPGym: Benchmarking Partially Observable Reinforcement Learning
Steven Morad, Ryan Kortvelesy, Matteo Bettini +2
Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contem…