CS 294: Deep Reinforcement Learning, Spring 2017

Instructors: Sergey Levine, John Schulman, Chelsea Finn
Lectures: Mondays and Wednesdays, 9:00am-10:30am in 306 Soda Hall.
Office Hours: MW 10:30-11:30, by appointment (see signup sheet on Piazza)
Communication: Piazza will be used for announcements, general questions and discussions, clarifications about assignments, student questions to each other, and so on. To sign up, go to Piazza and sign up with “UC Berkeley” and “CS294-112”.
For people who are not enrolled, but interested in following and discussing the course, there is a subreddit forum here: reddit.com/r/berkeleydeeprlcourse/
Please do not email the course instructors about MuJoCo licenses if you are not enrolled in the course. Unfortunately, we do not have any license that we can provide to students who are not officially enrolled in the course for credit.




Table of Contents




Lecture Videos

The course lectures are available below. The course is not being offered as an online course, and the videos are provided only for your personal informational and entertainment purposes. They are not part of any course requirement or degree-bearing university program.
For all videos, click here.
For live stream, click here.

Lectures, Readings, and Assignments

Below you can find an outline of the course. Slides and references will be posted as the course proceeds.

Prerequisites

CS189 or equivalent is a prerequisite for the course. 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

For introductory material on machine learning and neural networks, see

John's lecture series at MLSS

  • Lecture 1: intro, derivative free optimization
  • Lecture 2: score function gradient estimation and policy gradients
  • Lecture 3: actor critic methods
  • Lecture 4: trust region and natural gradient methods, open problems

Courses

Relevant Textbooks

Previous Offerings

An abbreviated version of this course was offered in Fall 2015.