Publication Date

Spring 2022

Advisor(s) - Committee Chair

Guangming Xing (Director), Huanjing Wang, Rong Yang

Degree Program

Department of Mathematics and Computer Science

Degree Type

Master of Science


In the past decade, machine learning strategies centered on the use of Deep Neural Networks (DNNs) have caught the interest of researchers due to their success in complicated classification and prediction problems. More recently, these DNNs have been applied to reinforcement learning tasks with state of- the-art results using Deep Q-Networks (DQNs) based on the Q-Learning algorithm. However, the DQN training process is different from standard DNNs and poses significant challenges for certain reinforcement learning environments. This paper examines some of these challenges, compares proposed solutions, and offers novel solutions based on previous research. Experiment implementation available at


Artificial Intelligence and Robotics