Learning temporal and structural credit assignments for reinforcement learning and experimental design

Ren, Zhizhou

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https://hdl.handle.net/2142/115591 Copy

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.

Graduation Semester

2022-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/115591

Copyright and License Information

Copyright 2022 Zhizhou Ren

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