{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115591"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115591","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning temporal and structural credit assignments for reinforcement learning and experimental design","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Ren, Zhizhou"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Peng, Jian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["credit assignment","reinforcement learning","experimental design"],"languages":["en","eng"],"rights":["Copyright 2022 Zhizhou Ren"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115591","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Jian"]},{"key":"dc:creator","label":"Author","values":["Ren, Zhizhou"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-25"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["credit assignment","reinforcement learning","experimental design"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Zhizhou Ren"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115591"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning temporal and structural credit assignments for reinforcement learning and experimental design"]}]}],"canonical_facts":{"dc:contributor":["Peng, Jian"],"dc:creator":["Ren, Zhizhou"],"dc:date":["2022-05","2022-04-25"],"dc:description":["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","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115591"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Zhizhou Ren"],"dc:subject":["credit assignment","reinforcement learning","experimental design"],"dc:title":["Learning temporal and structural credit assignments for reinforcement learning and experimental design"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}