2026-09-20T04:06:21Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/115591 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Peng, Jian 2022-05 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01 The student, Zhizhou Ren, accepted the attached license on 2022-04-21 at 10:16. The student, Zhizhou Ren, submitted this Thesis for approval on 2022-04-21 at 10:18. This Thesis was approved for publication on 2022-04-25 at 11:35. DSpace SAF Submission Ingestion Package generated from Vireo submission #17575 on 2022-11-11 at 12:11:36 Learning temporal and structural credit assignments for reinforcement learning and experimental design Ren, Zhizhou 2022-04-25 credit assignment reinforcement learning experimental design 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. Thesis eng https://hdl.handle.net/2142/115591 Copyright 2022 Zhizhou Ren M.S. Thesis Computer Science University of Illinois at Urbana-Champaign Computer Science