  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.
