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?**

**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