ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture. - Ryan Kortvelesy; Amanda Prorok (ICRA, 2021)

The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts. - Amanda Prorok; Jan Blumenkamp; Qingbiao Li; Ryan Kortvelesy; Zhe Liu 022; Ethan Stump (AAMAS, 2022)

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

Modeling Partially Observable Systems using Graph-Based Memory and Topological Priors. - Steven D. Morad; Stephan Liwicki; Ryan Kortvelesy; Roberto Mecca; Amanda Prorok (L4DC, 2022)

QGNN: Value Function Factorisation with Graph Neural Networks. - Ryan Kortvelesy; Amanda Prorok (CoRR, 2022)

Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning. - Ryan Kortvelesy; Steven D. Morad; Amanda Prorok (AAMAS, 2023)

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

Modeling Aggregate Downwash Forces for Dense Multirotor Flight. - Jennifer Gielis; Ajay Shankar; Ryan Kortvelesy; Amanda Prorok (ISER, 2023)

Generalised f-Mean Aggregation for Graph Neural Networks. - Ryan Kortvelesy; Steven D. Morad; Amanda Prorok (NeurIPS, 2023)

Reinforcement Learning with Fast and Forgetful Memory. - Steven D. Morad; Ryan Kortvelesy; Stephan Liwicki; Amanda Prorok (NeurIPS, 2023)

Fixed Integral Neural Networks. - Ryan Kortvelesy (CoRR, 2023)

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

Recurrent Reinforcement Learning with Memoroids. - Steven D. Morad; Chris Lu 001; Ryan Kortvelesy; Stephan Liwicki; Jakob N. Foerster; Amanda Prorok (NeurIPS, 2024)

Revisiting Recurrent Reinforcement Learning with Memory Monoids. - Steven D. Morad; Chris Lu 001; Ryan Kortvelesy; Stephan Liwicki; Jakob N. Foerster; Amanda Prorok (CoRR, 2024)

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