Advisor(s) - Committee Chair
Guangming Xing (Director), Huanjing Wang, Rong Yang
Department of Mathematics and Computer Science
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 https://github.com/caleb98/dqlearning.
Artificial Intelligence and Robotics
Cassady, Caleb, "Reinforcement Learning with Deep Q-Networks" (2022). Masters Theses & Specialist Projects. Paper 3554.