DOI: https://doi.org/10.15368/theses.2018.58
Available at: https://digitalcommons.calpoly.edu/theses/1846
Date of Award
6-2018
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
Advisor
Andrew Danowitz
Abstract
Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.
Included in
Computational Engineering Commons, Controls and Control Theory Commons, Robotics Commons