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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Student Name: Zaiwei Chen
Machine Learning Ph.D. Student
Home School: Aerospace Engineering
Georgia Institute of Technology
1 Dr. John-Paul Clarke (Advisor, School of Industrial and Systems Engineering, School of Aerospace Engineering, Georgia Institute of Technology)
2 Dr. Siva Theja Maguluri (Co-advisor, School of Industrial and Systems Engineering, Georgia Institute of Technology)
3 Dr. Justin Romberg (School of Electrical and Computer Engineering, Georgia Institute of Technology)
4 Dr. Benjamin Van Roy, Department of Electrical Engineering, Department of Management Science & Engineering, Stanford University) (external)
Reinforcement Learning (RL) captures an important facet of machine learning going beyond prediction and regression: sequential decision making, and has had a great impact on various problems of practical interest. The goal of this proposed thesis is to provide theoretical performance guarantees of RL algorithms. Specifically, we develop a universal approach for establishing finite-sample convergence bounds of RL algorithms when using tabular representation and when using function approximation. To achieve that, we consider general stochastic approximation algorithms and study their convergence bounds using a novel Lyapunov approach. The results enable us to gain insight into the behavior of RL algorithms.
