Course Information

Deep Reinforcement Learning - Spring 2020
Instructor: Prof. Songhwai Oh (오성회)
Email: songhwai (at)
Office Hours: Friday 2:00-4:00PM
Office: Building 133 Room 405
Course Number: 430.729 (003)
Time: M/W 11:00-12:15PM
Location: Building 301 Room 102
TA: Jaegu Choy (최재구)
Email: jaegu.choy (at)
Office: Building 133 Room 610

Course Description

With recent developments in deep learning, deep reinforcement learning is getting attention as it can solve an increasing number of complex problems, including the classic game of Go, video games, self-driving vehicles, and robot manipulation. In this course, we will review recent advances in deep reinforcement learning. We will first review Markov decision processes (MDP) and traditional reinforcement learning techniques. Then we will review recent developments in robot learning, deep learning, and deep reinforcement learning, including topics such as behavior cloning, inverse reinforcement learning, policy gradient, deep Q-network (DQN), generative adversarial networks (GAN), and generative adversarial imitation learning. This is an advanced graduate course and substantial reading and programming assignments will be assigned. Students are expected to participate actively in class. Lectures will be in English.





  • (430.457) Introduction to Intelligent Systems (지능시스템개론).
  • Also requires strong background in algorithms, linear algebra, and probability.