Learning temporal and structural credit assignments for reinforcement learning and experimental design
Ren, Zhizhou
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
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
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.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.