2026-09-18T17:43:33Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/121555 2023-12-13 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

Sreenivas, Ramavarapu S 2023-08 application/pdf en text 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 Deep reinforcement learning for quadrupeds Dashpute, Chinmay 2023-07-20 Quadrupeds Deep Reinforcement Learning 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. Text eng https://hdl.handle.net/2142/121555 Copyright 2023 Chinmay Dashpute M.S. Thesis Systems & Entrepreneurial Engr University of Illinois at Urbana-Champaign Industrial&Enterprise Sys Eng