We will introduce the fundamental concepts of reinforcement learning. Topics include, but not limited to, Markov Decision Process, Deep Q-Learning, Temporal-Difference Learning, Policy gradient methods, Actor-Critic method. The students will learn how to apply these to solve real-world problems via trial-and-error interaction. Basic knowledge of deep learning and Python is needed. Students should seek the instructor’s approval to take this course.