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.
@article{arxiv.1910.08926,
title = {Policy Learning for Malaria Control},
author = {Van Bach Nguyen and Belaid Mohamed Karim and Bao Long Vu and Jörg Schlötterer and Michael Granitzer},
journal= {arXiv preprint arXiv:1910.08926},
year = {2019}
}