# Policy Learning for Malaria Control

arXiv: https://arxiv.org/abs/1910.08926 · HTML: https://mlsanity.com/paper/1910.08926/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [An Analysis of Reinforcement Learning for Malaria Control](https://mlsanity.com/paper/2107.08988/) (2021)
- [Synthetic Data Generator for Adaptive Interventions in Global Health](https://mlsanity.com/paper/2303.01954/) (2023)
- [Adaptive Interventions for Global Health: A Case Study of Malaria](https://mlsanity.com/paper/2303.02075/) (2023)

## Abstract (arXiv)

Sequential decision making is a typical problem in reinforcement learning with plenty of algorithms to solve it. However, only a few of them can work effectively with a very small number of observations. In this report, we introduce the progress to learn the policy for Malaria Control as a Reinforcement Learning problem in the KDD Cup Challenge 2019 and propose diverse solutions to deal with the limited observations problem. We apply the Genetic Algorithm, Bayesian Optimization, Q-learning with sequence breaking to find the optimal policy for five years in a row with only 20 episodes/100 evaluations. We evaluate those algorithms and compare their performance with Random Search as a baseline. Among these algorithms, Q-Learning with sequence breaking has been submitted to the challenge and got ranked 7th in KDD Cup.