Ph.D. Thesis Proposal - A Unified Framework for Finite-Sample Analysis of Reinforcement Learning Algorithms

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    • Monday November 16, 2020
      4:30 pm - 6:00 pm
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Summary Sentence: ML Ph.D. student Zaiwei Chen presents his thesis proposal.

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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.

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GVU Center, ML@GT

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  • Created By: ablinder6
  • Workflow Status: Published
  • Created On: Nov 16, 2020 - 9:49am
  • Last Updated: Nov 16, 2020 - 9:49am