Course Information

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.

Announcements

• List of Deep Reinforcement Learning Papers
• [02/23] Please read Ethics of Learning.

Schedule

• Week 1:
  • 03/03: Introduction
    
    • Ch. 3 from Reinforcement Learning: An Introduction; Ch. 17 from AIMA
• Week 2:
  • 03/08: Review on MDPs
    • Deep Learning Short Course
    • Ch. 3 from Reinforcement Learning: An Introduction; Ch. 17 from AIMA
  • 03/10: Review on POMDPs, RL algorithms
    • Ch. 4, Ch. 6 from Reinforcement Learning: An Introduction; Ch. 21 from AIMA

References

• Reinforcement Learning: An Introduction (2018, 2nd Edition) Richard S. Sutton, Andrew G. Barto
• Artificial Intelligence: A Modern Approach (3rd edition), Stuart Russell and Peter Norvig, Prentice Hall, 2009. (AIMA Website)
• Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K. I. Williams, The MIT Press, 2006.

Prerequisites

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