5 citations · 8 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 5 cited
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…
cs.RO2022★ 3 cited
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
Matteo Bettini, Ryan Kortvelesy, Jan Blumenkamp +1
While many multi-robot coordination problems can be solved optimally by exact algorithms, solutions are often not scalable in the number of robots. Multi-Agent Reinforcement Learni…