{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121362"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121362","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Robust model-based reinforcement learning using L1 adaptive control","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01","abstract_has_math":false,"creators":["Karumanchi, Sambhu Harimanas"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Hovakimyan, Naira"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:24:57Z","subjects":["Robust Adaptive Control","Reinforcement Learning"],"languages":["en","eng"],"rights":["Copyright 2023 Sambhu Harimanas Karumanchi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121362","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hovakimyan, Naira"]},{"key":"dc:creator","label":"Author","values":["Karumanchi, Sambhu Harimanas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-07-17"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"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":["Robust Adaptive Control","Reinforcement Learning"]}]},{"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 2023 Sambhu Harimanas Karumanchi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121362"]}]},{"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 2025-08-01","The student, Sambhu Harimanas Karumanchi, accepted the attached license on 2023-07-12 at 22:42.","The student, Sambhu Harimanas Karumanchi, submitted this Thesis for approval on 2023-07-12 at 23:02.","This Thesis was approved for publication on 2023-07-17 at 16:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19669 on 2023-12-04 at 17:18:39","We introduce L1-MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input. Our approach approximates the transition function along each trajectory with a control-affine model to generate a control input augmentation that perturbs the input produced by the underlying MBRL policy. The perturbation produced by the L1 adaptive control is designed to enhance the robustness of the system against uncertainties. Importantly, the proposed L1 adaptive control-based learning scheme is agnostic to the choice of MBRL algorithm and can be integrated seamlessly with many model-based RL systems in practice. The method exhibits superior performance and sample efficiency on multiple MuJoCo environments, both with and without system noise, as demonstrated by our numerical simulations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Robust model-based reinforcement learning using L1 adaptive control"]}]}],"canonical_facts":{"dc:contributor":["Hovakimyan, Naira"],"dc:creator":["Karumanchi, Sambhu Harimanas"],"dc:date":["2023-08","2023-07-17"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01","The student, Sambhu Harimanas Karumanchi, accepted the attached license on 2023-07-12 at 22:42.","The student, Sambhu Harimanas Karumanchi, submitted this Thesis for approval on 2023-07-12 at 23:02.","This Thesis was approved for publication on 2023-07-17 at 16:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19669 on 2023-12-04 at 17:18:39","We introduce L1-MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input. Our approach approximates the transition function along each trajectory with a control-affine model to generate a control input augmentation that perturbs the input produced by the underlying MBRL policy. The perturbation produced by the L1 adaptive control is designed to enhance the robustness of the system against uncertainties. Importantly, the proposed L1 adaptive control-based learning scheme is agnostic to the choice of MBRL algorithm and can be integrated seamlessly with many model-based RL systems in practice. The method exhibits superior performance and sample efficiency on multiple MuJoCo environments, both with and without system noise, as demonstrated by our numerical simulations."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121362"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Sambhu Harimanas Karumanchi"],"dc:subject":["Robust Adaptive Control","Reinforcement Learning"],"dc:title":["Robust model-based reinforcement learning using L1 adaptive control"],"dc:type":["text"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}