The goal of this course is to provide a clear account of the key concepts and solution algorithms of reinforcement learning. Topics include optimal control, dynamic programming (including policy iteration and value iteration) Markov decision processes, temporal-difference learning value approximation, policy approximation Q-learning and various reinforcement learning algorithms. Emphasis will be placed on trade-off between exploitation and exploration and the trade-off between the sub-optimality and tractability. We will learn how to formulate, analyze and solve various reinforcement learning problems. Applications in various fields will be also discussed.