Table of Contents

• Prerequisites
• Syllabus
• Lecture Videos
• Related Materials
• Previous Offerings

Prerequisites

This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning. Students who are not familiar with the concepts below are encouraged to brush up using the references provided right below this list. We’ll review this material in class, but it will be rather cursory.

• Reinforcement learning and MDPs
  • Definition of MDPs
  • Exact algorithms: policy and value iteration
  • Search algorithms
• Numerical Optimization
  • gradient descent, stochastic gradient descent
  • backpropagation algorithm
• Machine Learning
  • Classification and regression problems: what loss functions are used, how to fit linear and nonlinear models
  • Training/test error, overfitting.

For introductory material on RL and MDPs, see

• CS188 EdX course, starting with Markov Decision Processes I
• Sutton & Barto, Ch 3 and 4.
• For a concise intro to MDPs, see Ch 1-2 of Andrew Ng’s thesis
• David Silver’s course, links below

For introductory material on machine learning and neural networks, see

• Andrej Karpathy’s course
• Geoff Hinton on Coursera
• Andrew Ng on Coursera
• Yaser Abu-Mostafa’s course

Source: CS 294 Deep Reinforcement Learning, Spring 2017