{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121555"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121555","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Deep reinforcement learning for quadrupeds","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Chinmay Dashpute, accepted the attached license on 2023-07-20 at 11:16.","The student, Chinmay Dashpute, submitted this Thesis for approval on 2023-07-20 at 12:10.","This Thesis was approved for publication on 2023-07-20 at 13:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19746 on 2023-12-04 at 17:03:33","This work presents a thesis on understanding different methods and frameworks developed for Deep Reinforce- ment Learning, and implementing procedure and methods outlined in [1] to develop a control policy for the Stanford Pupper quadruped robot [2]. The project involves simulating the Augmented Random Search (ARS) policy and DeepRL algorithm framework [1] using PyBullet to optimize the movement of the quadruped. The effect of different parameters of the ARS policy and DeepRL algorithm is studied in simulation to evaluate the outcomes and compare their performance and effectiveness."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Deep reinforcement learning for quadrupeds"]}]}],"canonical_facts":{"dc:contributor":["Sreenivas, Ramavarapu S"],"dc:creator":["Dashpute, Chinmay"],"dc:date":["2023-08","2023-07-20"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Chinmay Dashpute, accepted the attached license on 2023-07-20 at 11:16.","The student, Chinmay Dashpute, submitted this Thesis for approval on 2023-07-20 at 12:10.","This Thesis was approved for publication on 2023-07-20 at 13:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19746 on 2023-12-04 at 17:03:33","This work presents a thesis on understanding different methods and frameworks developed for Deep Reinforce- ment Learning, and implementing procedure and methods outlined in [1] to develop a control policy for the Stanford Pupper quadruped robot [2]. The project involves simulating the Augmented Random Search (ARS) policy and DeepRL algorithm framework [1] using PyBullet to optimize the movement of the quadruped. 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