Novel Exploration Techniques (NETs) for Malaria Policy Interventions

Authors

  • Oliver Bent University of Oxford
  • Sekou Remy IBM Research Africa
  • Stephen Roberts University of Oxford
  • Aisha Walcott-Bryant IBM Research Africa

DOI:

https://doi.org/10.1609/aaai.v32i1.11410

Keywords:

Malaria, Reinforcement Learning, Bandit, Gaussian Process

Abstract

The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity. Healthcare policy makers across the globe are facing problems under challenging constraints, with limited tools to help them make data driven decisions. In this work we frame the process of finding an optimal malaria policy as a stochastic multi-armed bandit problem, and implement three agent based strategies to explore the policy space. We apply a Gaussian Process regression to the findings of each agent, both for comparison and to account for stochastic results from simulating the spread of malaria in a fixed population. The generated policy spaces are compared with published results to give a direct reference with human expert decisions for the same simulated population. Our novel approach provides a powerful resource for policy makers, and a platform which can be readily extended to capture future more nuanced policy spaces.

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Published

2018-04-27

How to Cite

Bent, O., Remy, S., Roberts, S., & Walcott-Bryant, A. (2018). Novel Exploration Techniques (NETs) for Malaria Policy Interventions. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11410