Abstract
Our society is increasingly interconnected, making it easy for cascades/epidemic (diseases, disinformation etc). Current epidemic control efforts are based on approximate network epidemic models, which often ignore the unique complexity and rich information embedded in the complex interconnections of real-world networks/populations.Deep reinforcement learning (RL) is a powerful tool at learning policies for these nonlinear, complex processes in high-dimension. To control an epidemic outbreak on a Susceptible-Infected-Susceptible network epidemic model, we design a RL framework with a custom reward structure using the node2vec embedding technique. Results indicate deep RL is able to determine and converge on an optimal intervention policy in a relatively short time.
Thesis Completion
2019
Semester
Summer
Thesis Chair/Advisor
Enyioha, Chinwendu
Co-Chair
Shuai, Zhisheng
Degree
Bachelor of Science (B.S.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Language
English
Access Status
Open Access
Release Date
8-1-2019
Recommended Citation
Kerrigan, Alec H., "Reinforcement Learning for Optimal Control of Network Epidemic Processes" (2019). Honors Undergraduate Theses. 580.
https://stars.library.ucf.edu/honorstheses/580
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