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: TBD |
Communication: Piazza will be used for announcements, general questions about the course, clarifications about assignments, student questions to each other, discussions about material, and so on. To sign up, go to the Piazza website and sign up with “UC Berkeley” and “CS294-112” for your school and class. |
**Wait List**: If you are not a graduate student in EECS and are interested in taking this class, please fill out this form. |
Table of Contents
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
Syllabus
The syllabus will be made available in early 2017.
Lecture Videos
The course may be recorded this year. John also gave a lecture series at MLSS, and videos are available:
- 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
Related Materials
Courses
- Dave Silver’s course on reinforcement learning / Lecture Videos
- Nando de Freitas’ course on machine learning
- Andrej Karpathy’s course on neural networks
Relavent Textbooks
- Sutton & Barto, Reinforcement Learning: An Introduction
- Szepesvari, Algorithms for Reinforcement Learning
- Bertsekas, Dynamic Programming and Optimal Control, Vols I and II
- Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming
- Powell, Approximate Dynamic Programming