{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122173"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122173","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Adaptive learning from demonstration in heterogeneous agents: Concurrent minimization and maximization of surprise in sparse reward environments","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Clark, Emma"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Mehr, Negar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Reinforcement Learning","Learning From Demonstration","Curriculum Learning"],"languages":["en","eng"],"rights":["Copyright 2023 Emma Clark"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122173","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mehr, Negar"]},{"key":"dc:creator","label":"Author","values":["Clark, Emma"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-05"]},{"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":["Reinforcement Learning","Learning From Demonstration","Curriculum 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 Emma Clark"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122173"]}]},{"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 2025-12-01","The student, Emma Clark, accepted the attached license on 2023-12-04 at 10:25.","The student, Emma Clark, submitted this Thesis for approval on 2023-12-04 at 10:34.","This Thesis was approved for publication on 2023-12-05 at 17:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20124 on 2024-03-01 at 13:32:39","Learning from Demonstration (LfD) is a reinforcement learning method where an agent learns a policy by imitation demonstrations from an expert. This expert can be another agent, already trained to have an optimal policy or a predefined control system. Or, the expert can be a human. LfD is useful for learning very complex tasks or in settings with strict behavior guidelines or restrictions. One of the major limitations of LfD is an inability to learn when there are differences in dynamics between the student and teacher agents. This limits LfD methods to homogenous agents; however, real-world scenarios may often have differences in dynamics or environmental constraints between the student and teacher. Such as, a robot learning from human demonstration, or two different models of robot with variations in maximum joint angles or actuator power. Even analogous systems may have small variations in robot capabilities, due to noise or under-performance from technological limitations. To address this challenge, we propose a Student-Teacher framework, where the Teacher agent uses the Student’s surprise with, respect to demonstration trajectories, to infer differences in dynamics between itself and the Student. The teacher is then able to adapt its demonstration trajectories to consider the dynamics or constraints of the Student. In contrast to most common LfD methods, we assume the Teacher is not already an expert, but instead is learning in parallel to the Student."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Adaptive learning from demonstration in heterogeneous agents: Concurrent minimization and maximization of surprise in sparse reward environments"]}]}],"canonical_facts":{"dc:contributor":["Mehr, Negar"],"dc:creator":["Clark, Emma"],"dc:date":["2023-12","2023-12-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Emma Clark, accepted the attached license on 2023-12-04 at 10:25.","The student, Emma Clark, submitted this Thesis for approval on 2023-12-04 at 10:34.","This Thesis was approved for publication on 2023-12-05 at 17:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20124 on 2024-03-01 at 13:32:39","Learning from Demonstration (LfD) is a reinforcement learning method where an agent learns a policy by imitation demonstrations from an expert. This expert can be another agent, already trained to have an optimal policy or a predefined control system. Or, the expert can be a human. LfD is useful for learning very complex tasks or in settings with strict behavior guidelines or restrictions. One of the major limitations of LfD is an inability to learn when there are differences in dynamics between the student and teacher agents. This limits LfD methods to homogenous agents; however, real-world scenarios may often have differences in dynamics or environmental constraints between the student and teacher. Such as, a robot learning from human demonstration, or two different models of robot with variations in maximum joint angles or actuator power. Even analogous systems may have small variations in robot capabilities, due to noise or under-performance from technological limitations. To address this challenge, we propose a Student-Teacher framework, where the Teacher agent uses the Student’s surprise with, respect to demonstration trajectories, to infer differences in dynamics between itself and the Student. The teacher is then able to adapt its demonstration trajectories to consider the dynamics or constraints of the Student. In contrast to most common LfD methods, we assume the Teacher is not already an expert, but instead is learning in parallel to the Student."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122173"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Emma Clark"],"dc:subject":["Reinforcement Learning","Learning From Demonstration","Curriculum Learning"],"dc:title":["Adaptive learning from demonstration in heterogeneous agents: Concurrent minimization and maximization of surprise in sparse reward environments"],"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":"2026-07-22T22:25:00Z"}