paper-conference

Generalised $f$-Mean Aggregation for Graph Neural Networks
A generalised learnable aggregator that can boost the representational complexity of Graph Neural Networks.
Ryan Kortvelesy, Steven Morad, Amanda Prorok
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Generalised $f$-Mean Aggregation for Graph Neural Networks
Reinforcement Learning with Fast and Forgetful Memory
A fast memory model for reinforcement learning.
Steven Morad, Ryan Kortvelesy, Stephan Liwicki, Amanda Prorok
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Reinforcement Learning with Fast and Forgetful Memory
POPGym: Benchmarking Partially Observable Reinforcement Learning
A suite of partially observable gym environments for benchmarking memory models in reinforcement learning.
Steven Morad, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki, Amanda Prorok
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POPGym: Benchmarking Partially Observable Reinforcement Learning
Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning
A set autoencoder that defines a bijective mapping between variable-sized sets and fixed-sized embeddings.
Ryan Kortvelesy, Steven Morad, Amanda Prorok
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Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning
Fixed Integral Neural Networks
A method for analytically computing integrals over neural networks.
Ryan Kortvelesy
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Fixed Integral Neural Networks
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
A vectorised simulation environment for reinforcement learning.
Matteo Bettini, Ryan Kortvelesy, Jan Blumenkamp, Amanda Prorok
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VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
QGNN: Value Function Factorisation with Graph Neural Networks
A GNN-based value factorisation method, enabling credit assignment on collaborative tasks (i.e. tasks with interdependencies between agents).
Ryan Kortvelesy, Amanda Prorok
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QGNN: Value Function Factorisation with Graph Neural Networks
An example conference paper
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Ryan Kortvelesy, Robert Ford
Jul 1, 2013
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An example conference paper