Policy Learning for Malaria Control

2019-10-20 · Van Bach Nguyen, Belaid Mohamed Karim, Bao Long Vu, Jörg Schlötterer, Michael Granitzer

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.

📄 PDF Abstract BibTeX arXiv:1910.08926

Code (2)

Karim-53/Reinforcement-Learning-Malaria-Control-KDDCup-2019 공식 구현

bach1292/KDD_Cup_2019_LOLS_Team 공식 구현

Tasks

Bayesian OptimizationDecision MakingQ-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Sequential Decision Making

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