Reinforcement Learning Environment for Orbital Station-Keeping

Author

Armando Herrera III, The University of Texas Rio Grande Valley

Date of Award

12-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Dong-Chul Kim

Second Advisor

Dr. Emmett Tomai

Third Advisor

Dr. Zhixiang Chen

Abstract

In this thesis, a Reinforcement Learning Environment for orbital station-keeping is created and tested against one of the most used Reinforcement Learning algorithm called Proximal Policy Optimization (PPO). This thesis also explores the foundations of Reinforcement Learning, from the taxonomy to a description of PPO, and shows a thorough explanation of the physics required to make the RL environment. Optuna optimizes PPO's hyper-parameters for the created environment via distributed computing. This thesis then shows and analysis the results from training a PPO agent six times.

Comments

Copyright 2020 Armando Herrera III. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/reinforcement-learning-environment-orbital/docview/2556342314/se-2?accountid=7119

Recommended Citation

Herrera, Armando III, "Reinforcement Learning Environment for Orbital Station-Keeping" (2020). Theses and Dissertations. 677.
https://scholarworks.utrgv.edu/etd/677