Unsupervised Resource Allocation with Graph Neural Networks

Miles Cranmer, Peter Melchior, Brian Nord

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


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 needs to select—based on limited initial information—among 109 galaxies those whose detailed measurement will lead to optimal inference of the composition of the universe. Our technique presents a way of flexibly learning an allocation strategy by only requiring forward simulators for the physics of interest and the measurement process. We anticipate that our technique will also find applications in a range of allocation problems from social science studies to customer satisfaction surveys and exploration strategies of autonomous agents.

Original languageEnglish (US)
Pages (from-to)272-284
Number of pages13
JournalProceedings of Machine Learning Research
StatePublished - 2021
EventNeurIPS 2020 Workshop on Pre-Registration in Machine Learning - Virtual, Online
Duration: Dec 11 2020 → …

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


  • Graph Neural Networks
  • Resource Allocation


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