Director of Research (if dissertation) or Advisor (if thesis)
Sreenivas, Ramavarapu S
Department of Study
Industrial&Enterprise Sys Eng
Discipline
Systems & Entrepreneurial Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Quadrupeds
Deep Reinforcement Learning
Abstract
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.
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