DISSERTATION DEFENSE

Author :Nawras Alkassab
Advisors: Dr. Chin-Tser Huang
Date: October 19, 2026
Time: 03:15 pm
Location: Room 2267, Storey Innovation building

Abstract
In psychology, humans who are intrinsically motivated tend to outperform those whose self-validation is more dependent on extrinsic rewards. In reinforcement learning, curiosity-driven agents suffer from the noisy TV problem, since calculating the error in predicting the future observations leaves the agents perplexed in extremely randomized environments. In this work, we tackle the video prefetching problem at edge networks, an NP-Hard problem, using intrinsically motivated reinforcement learning agents. The extrinsic rewards in the video prefetching problem are sparse and delayed, since they are received from access networks by edge networks infrequently. First, we explore the benefits and schemes of video prefetching from cloud networks to edge networks. Next, we explore the design and implementation of deep reinforcement learning for video prefetching at edge networks. By formulating the prefetching problem at edge networks as Partially Observable Markov Decision Process, we propose an intrinsically motivated reinforcement learning agent, Techie, to maximize both prefetching accuracy and prefetching coverage. Techie self-tunes its aggressiveness to manage the trade-offs between prefetching accuracy and prefetching coverage, given a long-term trajectory of video requests. Finally, we conclude that the prefetching problem at edge networks is susceptible to large action space and observation space, which exponentially increases as the size of the edge network's storage increases linearly. In addition, we believe that intrinsically motivated reinforcement learning agents for video prefetching at edge networks offer multiple benefits such as their sensitivity to the popularity of video items issued by end-users at different heterogeneous access networks and their ability to make intelligent prefetching decisions online without relying on access networks' metrics such as Quality of Experience metrics and bandwidth wastage.