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.
Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. 2016.
Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. 1998
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
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. The course will cover the following topics: 1. From supervised learning to decision making 2. Basic reinforcement learning: Q-learning and policy gradients 3. Advanced model learning and prediction, distillation, reward learning 4. Advanced deep RL: trust region policy gradients, actor-critic methods, exploration 5. Open problems, research talks, invited lectures