5 citations · 8 across the 4 of their papers we have counts for
4 papers
Reinforcement Learning with Fast and Forgetful Memory
Steven Morad, Ryan Kortvelesy, Stephan Liwicki +1
Nearly all real world tasks are inherently partially observable, necessitating the use of memory in Reinforcement Learning (RL). Most model-free approaches summarize the trajectory…
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…
Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning
Ryan Kortvelesy, Steven Morad, Amanda Prorok
The problem of permutation-invariant learning over set representations is particularly relevant in the field of multi-agent systems -- a few potential applications include unsuperv…
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…