CS 285 at UC Berkeley
Deep Reinforcement Learning
Lectures: Mon/Wed 5-6:30 p.m., Online
IMPORTANT: If you are an undergraduate or 5th year MS student, or a non-EECS graduate student, please fill out this form to apply for enrollment into the Fall 2021 version of the course. Do not email the course instructors about enrollment -- all students who fill out the form will be reviewed. We will enroll off of this form during the first week of class. We will not be using the official CalCentral wait list, just this form.
Lecture recordings from the current (Fall 2021) offering of the course: watch here
Looking for deep RL course materials from past years?
Recordings of lectures from Fall 2020 are here, and materials from previous offerings are here.Week 2 Overview
Imitation Learning
Week 3 Overview
Intro to RL
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
Exploration
Week 9 Overview
Offline Reinforcement Learning
Week 10 Overview
RL Algorithm Design and Variational Inference
Week 11 Overview
Control as Inference and Inverse Reinforcement Learning
Week 12 Overview
Transfer Learning, Multi-Task Learning, and Meta-Learning
Week 13 Overview
Challenges and Open Problems
Lecture Slides
See Syllabus for more information.
- Lecture 1: Introduction and Course Overview
- Lecture 2: Supervised Learning of Behaviors
- 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: Optimal Control and Planning
- Lecture 11: Model-Based Reinforcement Learning
- Lecture 12: Model-Based Policy Learning
- Lecture 13: Exploration (Part 1)
- Lecture 14: Exploration (Part 2)
- Lecture 15: Offline Reinforcement Learning (Part 1)
- Lecture 16: Offline Reinforcement Learning (Part 2)
- Lecture 17: Reinforcement Learning Theory Basics
- Lecture 18: Variational Inference and Generative Models
- Lecture 19: Connection between Inference and Control
- Lecture 20: Inverse Reinforcement Learning
- Lecture 21: Transfer and Multi-Task Learning
- Lecture 22: Meta-Learning
- Lecture 23: Challenges and Open Problems