1 code implementation • 11 Mar 2024 • Dulhan Jayalath, Steven Morad, Amanda Prorok
Our objective is to learn a fixed-size latent Markov state from a variable number of agent observations.
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.
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.
2 code implementations • 2 Nov 2021 • Jan Blumenkamp, Steven Morad, Jennifer Gielis, QingBiao Li, Amanda Prorok
We demonstrate our framework on a case-study that requires tight coordination between robots, and present first-of-a-kind results that show successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on Adhoc communication.