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

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

Share

COinS