Reinforcement Learning for Optimization of COVID-19 Mitigation Policies
By Varun Kompella, Roberto Capobianco, Stacy Jong, Jonathan Browne, S. Fox, Lauren Ancel Meyers, Peter R. Wurman, Peter Stone. 2020
64 citations recorded by the source corpus.
The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date,the even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) can be used to optimize mitigation policies that minimize the economic impact without overwhelming the hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; and (2) an RL-based methodology for optimizing fine-grained mitigation policies within this simulator. Our results validate both the overall simulator behavior and the learned policies under realistic conditions.
Subfields: Epidemiology, Infectious Diseases, Modeling and Simulation, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Reinforcement Learning
Citations in the corpus
- The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models (2022, methodology): “To remedy this, we study the problem of reward hacking across four diverse environments: traffic control (Wu et al., 2021), COVID response (Kompella et al., 2020), blood glucose monitoring (Fox et al., 2020), and the Atari game Riverraid (Brockman et al., 2016).”