Comparison of RL Algorithms for Learning to Learn Problems

Author

Adolfo Gonzalez III, The University of Texas Rio Grande Valley

Date of Award

12-2019

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Dongchul Kim

Second Advisor

Dr. Zhixiang Chen

Third Advisor

Dr. Emmett Tomai

Abstract

Machine learning has been applied to many different problems successfully due to the expressiveness of neural networks and simplicity of first order optimization algorithms. The latter being a vital piece needed for training large neural networks efficiently. Many of these algorithms were produced with behavior produced by experiments and intuition. An interesting question that comes to mind is that rather than observing and then designing algorithms with beneficial behaviors, can these algorithms be learned through a reinforcement learning by modeling optimization as a game. This paper explores several reinforcement learning algorithms which are applied to learn policies suited for optimization.

Comments

Copyright 2019 Adolfo Gonzalez III. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/comparison-rl-algorithms-learning-learn-problems/docview/2382593692/se-2?accountid=7119

Recommended Citation

Gonzalez, Adolfo III, "Comparison of RL Algorithms for Learning to Learn Problems" (2019). Theses and Dissertations. 458.
https://scholarworks.utrgv.edu/etd/458