CS 285 at UC Berkeley
Deep Reinforcement Learning
Lectures: Mon/Wed 5-6:30 p.m., Wheeler 212
NOTE: We are holding an additional office hours session on Fridays from 2:30-3:30PM in the BWW lobby. The OH will be led by a different TA on a rotating schedule.
Lecture recordings from the current (Fall 2023) offering of the course: watch here
Looking for deep RL course materials from past years?
Recordings of lectures from Fall 2022 are here, and materials from previous offerings are here.Email all staff (preferred): cs285-staff-fa2023@lists.eecs.berkeley.edu
Week 2 Overview
Imitation Learning
Week 3 Overview
Intro to RL and Policy Gradients
Week 4 Overview
Actor Critic and Value Function Methods
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 13 Overview
Challenges and Open Problems
Week 14 Overview
Challenges and Open Problems
Homeworks
See Syllabus for more information (including rough schedule).
Lecture Slides
See Syllabus for more information.
- Lecture 1: Introduction and Course Overview
- Lecture 2: Supervised Learning of Behaviors
- Lecture 3: PyTorch Tutorial
- 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: RL with Sequence Models
- Lecture 22: Meta-Learning and Transfer Learning
- Lecture 23: Challenges and Open Problems