Learning temporal and structural credit assignments for reinforcement learning and experimental design
Ren, Zhizhou
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https://hdl.handle.net/2142/115591
Description
Title
Learning temporal and structural credit assignments for reinforcement learning and experimental design
Author(s)
Ren, Zhizhou
Issue Date
2022-04-25
Director of Research (if dissertation) or Advisor (if thesis)
Peng, Jian
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)
credit assignment
reinforcement learning
experimental design
Language
eng
Abstract
Credit assignment is a fundamental challenge for artificial intelligence, which refers to the attribution of a global outcome to each internal components within a large system. Recent advances in machine learning approaches aim to learn a credit assignment mechanism from the experience data so that the sparse and inexact environmental feedback can be decomposed to dense and local supervisions. In this thesis, we consider two scenarios of credit assignment problems, temporal credit assignment and structural credit assignment, corresponding to the applications of credit assignment methods to reinforcement learning and experimental design. Regarding these problems, we propose two algorithms to perform data-driven credit assignment and decompose the inexact environmental supervision. We present theoretical analysis to characterize the algorithmic properties of our credit assignment method and connect it with prior works in the literature. The experiment results show that our methods can effectively improve the sample efficiency of episodic reinforcement learning and protein sequence design.
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