In this course, we will learn and implement basic theory and practical algorithms of reinforcement learning (RL) to be capable of understanding research papers on RL and able to develop a new RL algorithm for a given problem. The course will focus on state of the art techniques including Deep Q-Learning, Policy Gradient, and Actor Critic. After studying basic algorithms, current scientific topics selected from recent Deep Reinforcement Learning research papers will be discussed.
Lecture
Standard Letter (A-F)
Department of Computer Science