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An Analysis of Reinforcement Learning for Malaria Control

arXiv 2107.08988 ↗First posted 19 Jul 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

Previous work on policy learning for Malaria control has often formulated the problem as an optimization problem assuming the objective function and the search space have a specific structure. The problem has been formulated as multi-armed bandits, contextual bandits and a Markov Decision Process in isolation. Furthermore, an emphasis is put on developing new algorithms specific to an instance of Malaria control, while ignoring a plethora of simpler and general algorithms in the literature. In this work, we formally study the formulation of Malaria control and present a comprehensive analysis of several formulations used in the literature. In addition, we implement and analyze several reinforcement learning algorithms in all formulations and compare them to black box optimization. In contrast to previous work, our results show that simple algorithms based on Upper Confidence Bounds are sufficient for learning good Malaria policies, and tend to outperform their more advanced counterparts on the malaria OpenAI Gym environment.

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References · 17

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2019Reinforcement Learning in Healthcare: A Surveycited by 308
  3. 2018Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendationcited by 101
  4. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  5. 2014Algorithms for multi-armed bandit problemscited by 63
  6. 2018Evaluating Reinforcement Learning Algorithms in Observational Health Settingscited by 60
  7. 2019Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adaptingcited by 42
  8. 2018Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Datacited by 23
  9. 2020Deep reinforcement learning for large-scale epidemic controlcited by 16
  10. 2017Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patientscited by 13
  11. 2020EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Modelscited by 11
  12. 2017Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimizationcited by 9
5 more
  1. 2013Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approachcited by 8
  2. 2015Optimization of anemia treatment in hemodialysis patients via reinforcement learningcited by 6
  3. 2018Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsiscited by 5
  4. 2017Novel Exploration Techniques (NETs) for Malaria Policy Interventionscited by 4
  5. 2019Policy Learning for Malaria Controlcited by 3

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