Deep reinforcement learning for large-scale epidemic control

by   Pieter Libin, et al.

Epidemics of infectious diseases are an important threat to public health and global economies. Yet, the development of prevention strategies remains a challenging process, as epidemics are non-linear and complex processes. For this reason, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza. Firstly, we construct a new epidemiological meta-population model, with 379 patches (one for each administrative district in Great Britain), that adequately captures the infection process of pandemic influenza. Our model balances complexity and computational efficiency such that the use of reinforcement learning techniques becomes attainable. Secondly, we set up a ground truth such that we can evaluate the performance of the 'Proximal Policy Optimization' algorithm to learn in a single district of this epidemiological model. Finally, we consider a large-scale problem, by conducting an experiment where we aim to learn a joint policy to control the districts in a community of 11 tightly coupled districts, for which no ground truth can be established. This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space. Moreover, through this experiment, we demonstrate that there can be an advantage to consider collaboration between districts when designing prevention strategies.



page 35


Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

Infectious disease outbreaks can have a disruptive impact on public heal...

Computational Flight Control: A Domain-Knowledge-Aided Deep Reinforcement Learning Approach

This papers aims to examine the potential of using the emerging deep rei...

Deep reinforcement learning for scheduling in large-scale networked control systems

This work considers the problem of control and resource scheduling in ne...

Deep Reinforcement Learning for Foreign Exchange Trading

Reinforcement learning can interact with the environment and is suitable...

A Deep Reinforcement Learning Approach for Composing Moving IoT Services

We develop a novel framework for efficiently and effectively discovering...

Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review

The cybersecurity threat landscape has lately become overly complex. Thr...

Hierarchical Policy Learning is Sensitive to Goal Space Design

Hierarchy in reinforcement learning agents allows for control at multipl...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.