# Unsupervised Resource Allocation with Graph Neural Networks

@article{Cranmer2021UnsupervisedRA, title={Unsupervised Resource Allocation with Graph Neural Networks}, author={M. Cranmer and Peter Melchior and Brian Nord}, journal={ArXiv}, year={2021}, volume={abs/2106.09761} }

We present an approach for maximizing a global utility function by learning how to allocate resources in an unsupervised way. We expect interactions between allocation targets to be important and therefore propose to learn the reward structure for near-optimal allocation policies with a GNN. By relaxing the resource constraint, we can employ gradient-based optimization in contrast to more standard evolutionary algorithms. Our algorithm is motivated by a problem in modern astronomy, where one… Expand

#### One Citation

Graph Neural Network-based Resource Allocation Strategies for Multi-Object Spectroscopy

- Computer Science, Physics
- ArXiv
- 2021

This work presents a bipartite Graph Neural Network architecture for trainable resource allocation strategies that enables fast adjustment and deployment of allocation strategies, statistical analyses of allocation patterns, and fully differentiable, science-driven solutions for resource allocation problems. Expand

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