CS 294-112 at UC Berkeley
Deep Reinforcement Learning
Lectures: Wed/Fri 10-11:30 a.m., Soda Hall, Room 306
The 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.
Enrollment is now closed. If you are not officially enrolled through CalCentral, please direct your questions to the subreddit.
Materials from previous offerings of this course are available here.
Week 1 Overview
Course Introduction, Imitation Learning
Week 3 Overview
Policy Gradients and Actor Critic
Week 4 Overview
Q-Learning
Week 5 Overview
Advanced Policy Gradients and Control
Week 6 Overview
Model Based Reinforcement Learning
Week 7 Overview
Model Based RL and Probabilistic Models
Week 8 Overview
Probabilistic Modeling of Behavior
Week 10 Overview
Exploration, Transfer, and Multi-task Learning
Week 11 Overview
RL Systems, Advanced Imitation, and Open Problems
Week 12 Overview
Guest Lectures
- Homework 5a: Advanced Topics - Exploration
- Homework 5b: Advanced Topics - Soft Actor-Critic
- Homework 5c: Advanced Topics - Meta-Learning
- Lecture 23: Guest Lecture: Craig Boutilier
- Lecture 24: Guest Lecture: Gregory Kahn
Week 13 Overview
Guest Lectures
- Homework 5a: Advanced Topics - Exploration
- Homework 5b: Advanced Topics - Soft Actor-Critic
- Homework 5c: Advanced Topics - Meta-Learning
- Lecture 25: Guest Lecture: Quoc Le & Barret Zoph
- Lecture 26: Guest Lecture: Karol Hausman (Canceled)
Homeworks
See Syllabus for more information.
Lecture Slides
See Syllabus for more information.
- Lecture 1: Introduction and Course Overview
- Lecture 2: Supervised Learning and Imitation
- Lecture 3: TensorFlow and Neural Nets Review Session (notebook)
- Lecture 4: Reinforcement Learning Introduction
- Lecture 5: Policy Gradients Introduction
- Lecture 6: Actor-Critic Introduction
- Lecture 7: Value Functions and Q-Learning
- Lecture 8: Advanced Q-Learning Algorithms
- Lecture 9: Advanced Policy Gradients
- Lecture 10: Optimal Control and Planning
- Lecture 11: Model-Based Reinforcement Learning
- Lecture 12: Advanced Model Learning and Images
- Lecture 13: Learning Policies by Imitating Other Policies
- Lecture 14: Probability and Variational Inference Primer
- Lecture 15: Connection between Inference and Control
- Lecture 16: Inverse Reinforcement Learning
- Lecture 17: Exploration: Part 1
- Lecture 18: Exploration: Part 2
- Lecture 19: Transfer Learning and Multi-Task Learning
- Lecture 20: Meta-Learning
- Lecture 21: Parallelism and RL System Design
- Lecture 22: Advanced Imitation Learning and Open Problems
- Lecture 23: Guest Lecture: Craig Boutilier
- Lecture 24: Guest Lecture: Gregory Kahn
- Lecture 25: Guest Lecture: Quoc Le & Barret Zoph
- Lecture 26: Guest Lecture: Karol Hausman (Canceled)
- Lecture 27: Final Project Presentations: Part 1 (No Slides)
- Lecture 28: Final Project Presentations: Part 2 (No Slides)