Publication Date
Fall 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Genya Ishigaki
Second Advisor
Thomas Austin
Third Advisor
Navrati Saxena
Keywords
Network Routing, Reinforcement Learning, Explainable Machine Learning, Explainable Reinforcement Learning.
Abstract
Software-defined Networking (SDN) provides a solution for configuring multiple network devices by offering a centralized controller architecture. Network routing is one of the most crucial problems in network configuration. In particular, the surging network traffic demands require efficient routing techniques to load balance communication links. In order to optimize the communication path’s utilization and reduce request blocking, this project utilizes Reinforcement Learning (RL) to decide the routes for given network requests. Furthermore, we adopt Explainable Reinforcement Learning (XRL) to explain the RL learning agent’s decision-making process to enhance the trustworthiness of our approach. In particular, we focus on Feature Importance (FI) in combination with supervised learning and SHapley Additive Explanations (SHAP) to identify critical communication links. This approach provides insights into the agent’s decision-making process for reducing the number of blocked requests in network routing.
Recommended Citation
Xiu, Yu, "Explainable Reinforcement Learning for Network Routing Optimization" (2024). Master's Projects. 1451.
https://scholarworks.sjsu.edu/etd_projects/1451
