Graduation Semester and Year
2019
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
Manfred Huber
Abstract
The framework of reinforcement learning is a powerful suite of algorithms that can learn generalized solutions to complex decision making problems. However, the applications of reinforcement learning algorithms to traditional machine learning problems such as clustering, classification and representation learning, have rarely been explored. With the advent of large amounts of data, robust models are required which can extract meaningful representations from the data that can potentially be applied to new unseen tasks. The presented work investigates the applications of reinforcement learning algorithms in the perspective of transfer learning by applying algorithms in the framework of reinforcement learning to address a variety of machine learning problems in order to learn concise abstractions useful for transfer.
Keywords
Machine learning, Reinforcement learning, Artificial neural networks, Representation learning
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Bose, Sourabh, "Learning Representations Using Reinforcement Learning" (2019). Computer Science and Engineering Dissertations. 48.
https://mavmatrix.uta.edu/cse_dissertations/48
Comments
Degree granted by The University of Texas at Arlington