CS 285 at UC Berkeley
Deep Reinforcement Learning
Lectures: Mon/Wed 10-11:30 a.m., Soda Hall, Room 306
Lectures will be streamed and recorded. 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.
Piazza is the preferred platform to communicate with the instructors. However, if for some reason you wish to contact the course staff by email, use the following email address: [email protected].
Looking for deep RL course materials from past years?
Recordings of lectures from fall 2018 are here, and materials from previous offerings are here.Week 1 Overview
Course Introduction, Imitation Learning
Week 2 Overview
Imitation Learning
Week 4 Overview
Policy Gradients and Actor Critic
Week 5 Overview
Value Functions and Q-learning
Week 6 Overview
Advanced Policy Gradients and Model-based learning
Week 7 Overview
Advanced Model Learning and Imitating Optimal Controllers
Week 8 Overview
Connection between inference and control
Week 9 Overview
Inverse Reinforcement Learning and multi-task learning
Week 10 Overview
Parallelism for RL
Week 11 Overview
Exploration
Week 13 Overview
Information Theory, Challenges, Open Problems
Homeworks
See Syllabus for more information.
Lecture Slides
See Syllabus for more information.
- Lecture 1: Introduction and Course Overview
- Lecture 2: Supervised Learning of Behaviors
- Lecture 3: TensorFlow and Neural Nets Review Session (notebook)
- Lecture 4: Introduction to Reinforcement Learning
- Lecture 5: Policy Gradients
- Lecture 6: Actor-Critic Algorithms
- Lecture 7: Value Function Methods
- Lecture 8: Deep RL with Q-functions
- Lecture 9: Advanced Policy Gradients
- Lecture 10: Model-based Planning
- Lecture 11: Model-based Reinforcement Learning
- Lecture 12: Model-based Policy Learning
- Lecture 13: Variational Inference and Generative Models
- Lecture 14: Control as Inference
- Lecture 15: Inverse Reinforcement Learning
- Lecture 16: Transfer and Multi-task Learning
- Lecture 17: Distributed RL
- Lecture 18: Exploration (Part 1)
- Lecture 19: Exploration (Part 2)
- Lecture 20: Meta-learning
- Lecture 21: Information Theory, Open Problems