Learning to Update: Using Reinforcement Learning to Discover Policies for List Update
Name
quaye-iquaye-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
2.65 MB
Format
Adobe PDF
Checksum (MD5)
cfe3660b41e341eeb5c6462424bf8fd4
Author(s)
Quaye, Isabelle A.
Advisor(s)
Rubinfeld, Ronitt
Indyk, Piotr
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
Abstract
The use of machine learning models in algorithms design is a rapidly growing f ield, often termed learning-augmented algorithms. A notable advancement in this field is the use of reinforcement learning for algorithm discovery. Developing algorithms in this manner offers certain advantages, novelty and adaptability being chief among them. In this thesis, we put reinforcement learning to the task of discovering an algorithm for the list update problem. The list update problem is a classic problem with applications in caching and databases. In the process of uncovering a new list update algorithm, we also prove a competitive ratio for the transposition heuristic, which is a well-known algorithm for the list update problem. Finally, we discuss key ideas and insights from the reinforcement learning agent that hints towards optimal behavior for the list update problem.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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