Term of Award
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mathematical Sciences
Dr. Stehpen Carden
Committee Member 1
Dr. Ionut Iacob
Committee Member 2
Dr. Zheni Utic
In reinforcement learning the process of selecting an action during the exploration or exploitation stage is difficult to optimize. The purpose of this thesis is to create an action selection process for an agent by employing a low discrepancy action selection (LDAS) method. This should allow the agent to quickly determine the utility of its actions by prioritizing actions that are dissimilar to ones that it has already picked. In this way the learning process should be faster for the agent and result in more optimal policies.
Lindborg, Jedidiah, "Reinforcement Learning: Low Discrepancy Action Selection for Continuous States and Actions" (2022). Electronic Theses and Dissertations. 2357.
Research Data and Supplementary Material
Applied Statistics Commons, Artificial Intelligence and Robotics Commons, Other Applied Mathematics Commons, Other Mathematics Commons