Date of Graduation
Master of Science in Industrial Engineering (MSIE)
Edward A. Pohl
Second Committee Member
Machine Learning, Network Reliability, Reinforcement Learning, Resource Allocation
Networks provide a variety of critical services to society (e.g. power grid, telecommunication, water, transportation) but are prone to disruption. With this motivation, we study a sequential decision problem in which an initial network is improved over time (e.g., by adding or increasing the reliability of edges) and rewards are gained over time as a function of the network’s all-terminal reliability. The actions during each time period are limited due to availability of resources such as time, money, or labor. To solve this problem, we utilized a Deep Reinforcement Learning (DRL) approach implemented within OpenAI-Gym using Stable Baselines. A Proximal Policy Optimization (PPO) was used to identify the edge to be improved or a new edge to be added based on the current state of the network and the available budget. To calculate the network’s all-terminal reliability, a reliability polynomial was employed. To understand how the model behaves under a variety of conditions, we explored numerous network configurations with different initial link reliability, added link reliability, number of nodes, and budget structures. We conclude with a discussion of insights gained from our set of designed experiments.
Wells, H. (2022). Using Reinforcement Learning to Improve Network Reliability through Optimal Resource Allocation. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/4714