1 code implementation • 15 Feb 2024 • Steven Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob Foerster, Amanda Prorok
Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov states.
1 code implementation • 26 Jul 2023 • Ryan Kortvelesy
In this work, we present a method for representing the analytical integral of a learned function $f$.
3 code implementations • 3 Mar 2023 • Steven Morad, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki, Amanda Prorok
Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory.
2 code implementations • 24 Feb 2023 • 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 unsupervised training of aggregation functions in graph neural networks (GNNs), neural cellular automata on graphs, and prediction of scenes with multiple objects.
1 code implementation • 7 Jul 2022 • Matteo Bettini, Ryan Kortvelesy, Jan Blumenkamp, Amanda Prorok
VMAS's scenarios prove challenging in orthogonal ways for state-of-the-art MARL algorithms.
1 code implementation • 25 May 2022 • Ryan Kortvelesy, Amanda Prorok
In multi-agent reinforcement learning, the use of a global objective is a powerful tool for incentivising cooperation.
no code implementations • 26 Jul 2021 • Amanda Prorok, Jan Blumenkamp, QingBiao Li, Ryan Kortvelesy, Zhe Liu, Ethan Stump
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances.
1 code implementation • 27 Jun 2021 • Steven D. Morad, Stephan Liwicki, Ryan Kortvelesy, Roberto Mecca, Amanda Prorok
Solving partially-observable Markov decision processes (POMDPs) is critical when applying reinforcement learning to real-world problems, where agents have an incomplete view of the world.
1 code implementation • 24 Mar 2021 • Ryan Kortvelesy, Amanda Prorok
Recent work in the multi-agent domain has shown the promise of Graph Neural Networks (GNNs) to learn complex coordination strategies.