Instructors: Sergey Levine, John Schulman, and Chelsea Finn.
This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning. The course covers topics: Supervised learning and decision making; Basic reinforcement learning: Q-learning and policy gradients; Advanced model learning and prediction; Advanced deep reinforcement learning: trust region policy gradients, actor-critic methods, exploration; Open problems and research talks.
Lecture 1: Introduction and course overview (Video) (Slides)
Lecture 2: Supervised learning and imitation (Video) (Slides)
Lecture 3: Reinforcement learning introduction (Video) (Slides)
Lecture 9: Learning dynamical systems from data (Video) (Slides)
Lecture 10: Learning policies by imitating optimal controllers (Video) (Slides)
Guest Lecture: Advanced model learning and images (Video) (Slides)
Lecture 11: Connection between inference and control (Video) (Slides)
Lecture 13 (Part 1): Advanced policy gradients (natural gradient, importance sampling) (Video) (Slides)
Lecture 14: Exploration (part 2) and transfer learning (Video) (Slides)
Lecture 15: Multi-task learning and transfer (Video) (Slides)
Lecture 17: Advanced imitation learning and open problems (Video) (Slides)
