CS 285 at UC Berkeley

# Deep Reinforcement Learning

Lectures: Mon/Wed 5:30-7 p.m., Online

Lectures will be recorded and provided before the lecture slot. The lecture slot will consist of discussions on the course content covered in the lecture videos.

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: cs285fall2020@googlegroups.com.

Lecture recordings from the current (Fall 2020) offering of the course: watch here

Enrolled students: please use the private link you were provided, not this one!

**Looking for deep RL course materials from past years?**

### 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 9 Overview

## Offline Reinforcement Learning and RL Theory

### Week 10 Overview

## RL Algorithm Design and Variational Inference

### Week 11 Overview

## Control as Inference and Inverse Reinforcement Learning

### Week 12 Overview

## Transfer Learning and Multi-Task Learning

### Week 13 Overview

## Meta-Learning, 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: Exploration (Part 1)
- Lecture 14: Exploration (Part 2)
- Lecture 15: Offline Reinforcement Learning
- Lecture 16: Introduction to RL Theory
- Lecture 17: Deep RL Algorithm Design
- Lecture 18: Probability and Variational Inference Primer
- Lecture 19: Connection between Inference and Control
- Lecture 20: Inverse Reinforcement Learning
- Lecture 21: Transfer Learning and Multi-Task Learning
- Lecture 22: Meta-Learning
- Lecture 23: Challenges and Open Problems