University of Illinois Urbana-Champaign

Learning and adaptation in graphs, networks, and autonomous systems

Lubars, Joseph

Content Files
LUBARS-DISSERTATION-2021.pdf

Permalink

https://hdl.handle.net/2142/113817

Description

Title
Learning and adaptation in graphs, networks, and autonomous systems
Author(s)
Lubars, Joseph
Issue Date
2021-10-06
Director of Research (if dissertation) or Advisor (if thesis)
Srikant, Rayadurgam
Doctoral Committee Chair(s)
Srikant, Rayadurgam
Committee Member(s)
  • Beck, Carolyn L
  • Hu, Bin
  • Varshney, Lav R
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2022-04-29T21:34:07Z
Keyword(s)
  • Reinforcement Learning
  • Autonomous Driving
  • Wireless Scheduling
Abstract
Reinforcement learning and adaptation are widely used in a variety of applications, whenever we want to control a dynamical system in some optimal manner. For example, in wireless networking, we can use these tools to learn to route packets more efficiently, leading to lower latency. In games, we can learn better strategies through self-play. In this dissertation, we focus on three problems of reinforcement learning and adaptation. We first consider a theoretical problem in reinforcement learning called optimistic policy iteration (OPI). We prove convergence of a variant of OPI whose convergence properties have been previously unknown. Next, we consider the problem of designing an algorithm to allow a car to autonomously merge onto a highway from an on-ramp. Two broad classes of techniques have been proposed to solve motion planning problems in autonomous driving: Model Predictive Control (MPC) and Reinforcement Learning (RL). In this dissertation, we present an algorithm which blends the model-free RL agent with the MPC solution and show that it provides better trade-offs between a number of relevant metrics: passenger comfort, efficiency, crash rate and robustness. Finally, we analyze a wireless scheduling problem with a limited probing constraint. We show how a throughput optimal solution can be computed, even without knowledge of the channel statistics. Interestingly, although this problem requires adaptability in the face of unknown conditions, we find that reinforcement learning is not needed for optimal performance and may even be harmful if applied without care.
Graduation Semester
2021-12
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/113817
Copyright and License Information
Copyright 2021 Joseph Lubars

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