Director of Research (if dissertation) or Advisor (if thesis)
Lazebnik, Svetlana
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Reinforcement Learning
Knowledge Distillation
Language
eng
Abstract
Optimizing sample efficiency, or the experience needed in an environment to gain satisfactory performance, is a core challenge for developing reinforcement learning agents. While imitation learning resolves this issue, it is constrained by expert performance. On the other hand, model-based strategies, which learn a world model of the environment, typically fail to approach the asymptotic performance of model-free approaches. In this thesis, we focus on combining imitation learning with model-free reinforcement learning to maximize sample efficiency and achieve higher asymptotic performance. We propose an intuitive approach to leveraging the strengths of each paradigm to produce higher rewards over a fixed number of frames when observing learned experts. We further investigate our method’s applicability to knowledge distillation for reduced-complexity agents. These studies and results lay the foundation for further study which will benefit model-free reinforcement learning as a whole.
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