CS 185/285 at UC Berkeley
Deep Reinforcement Learning
Lectures: 9 - 10 am on Wednesdays and 8 - 10 am on Fridays, both in Hearst Annex A1
Announcement: The default final project options are now available: Offline-to-Online RL Default Final Project and LLM RL Default Final Project.
Announcement: The final project outline has been released.
Looking for deep RL course materials from past years?
Recordings of lectures from Fall 2023 are here, and materials from previous offerings are here.Email all staff (preferred): [email protected]
Week 1 Overview
Course Intro & Imitation Learning
Week 2 Overview
Imitation Learning & RL Basics
Week 3 Overview
Policy Gradients & Actor Critic
Week 5 Overview
Advanced Policy Gradients
Week 6 Overview
Variational Inference
Week 7 Overview
Finishing VI & LLM RL
Week 9 Overview
Offline Reinforcement Learning
Week 10 Overview
Exploration & RL Theory
Week 11 Overview
Midterm Review
Week 12 Overview
Exploration & Multi-task RL
Week 14 Overview
Final Guest Lecture
Final Project Information
Default Project Options
Project Outline
We are grateful for generous compute support from Lecture Slides
See Syllabus for more information.
- Lecture 1: Introduction
- Lecture 2: Behavioral Cloning
- Lecture 3: Behavioral Cloning Part 2
- Lecture 4: RL Basics
- Lecture 5: Policy Gradients
- Lecture 6: Actor Critic
- Lecture 7: Value-Based RL
- Lecture 8: Q-learning in Practice
- Lecture 9: Advanced Policy Gradients Part 1
- Lecture 10: Advanced Policy Gradients Part 2
- Lecture 11: Variational Inference
- Lecture 12: VI in RL
- Lecture 13: Control as Inference
- Lecture 14: LLM RL
- Lecture 15: Model-Based RL Part 1
- Lecture 16: Model-Based RL Part 2
- Lecture 17: Offline RL Part 1
- Lecture 18: Offline RL Part 2
- Lecture 19: Exploration
- Lecture 20: RL Theory
- Lecture 21: Midterm Review Part 1
- Lecture 22: Midterm Review Part 2
- Lecture 23: Advanced Exploration
- Lecture 24: Multi-task RL
- Lecture 25: Challenges and Open Problems
Discussion Section Slides
See Syllabus for more information.
- Section 1: PyTorch Tutorial
- Section 2 Part 1: Probability Review
- Section 2 Part 2: BC Distributional Shift
- Section 3: Policy Gradients and Actor Critic
- Section 4: DQN and SAC
- Section 5: Advanced Policy Gradients
- Section 6: Variational Inference
- Section 7: IRL and LLM RL
- Section 8: Model-Based RL
- Section 9: Offline RL