Theses and Dissertations

Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Dr. Dongchul Kim

Second Advisor

Dr. Zhixiang Chen

Third Advisor

Dr. Emmett Tomai


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.


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