New!
Nanodegree Program

Become a Deep Reinforcement Learning Expert

Become a reinforcement learning expert

Learn the deep reinforcement learning skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics.

Enrollment Closing In

  • Time
    1 Four-Month Term

    Study 10-15 hrs/week and complete in 4 months.

  • Classroom Opens
    April 23, 2019

    Classroom opens 7 days after enrollment closes

In Collaboration with
  • Unity
  • NVIDIA Deep Learning Institute

Dream big. We’ll get you there!

Whatever your goals, Udacity is dedicated to helping you make them happen. Master the latest skills, build amazing projects, and advance your career. With our unrivaled support and personalized attention we’ll do everything we can to make sure you succeed. We’ve got the support you need to turn your dreams into reality.
  • Dedicated personal mentor

    Overcome barriers to your learning with a knowledgeable mentor who can answer your questions and keep you focused on your goals.

  • Weekly live sessions

    Get the close attention you need and interact with your classmates in regular live Q&A sessions and webinars.

  • Personalized learning plan

    Accelerate your learning and beat your goals with a learning plan designed around your life.

Why Take This Nanodegree Program?

Deep reinforcement learning is one of AI’s hottest fields. Researchers, engineers, and investors are excited by its world-changing potential. In this advanced program, you’ll master techniques like Deep Q-Learning and Actor-Critic Methods, and connect with experts from NVIDIA and Unity as you build a portfolio of your own reinforcement learning projects.


Why Take This Nanodegree Program?

Apple, Facebook, and Google are investing in deep reinforcement learning.

Master the Most Cutting-Edge Techniques
Master the Most Cutting-Edge Techniques

Master the Most Cutting-Edge Techniques

Deep reinforcement learning is at the forefront of AI research. Many experts see it as a path to Artificial General Intelligence. In this advanced program, you’ll master the latest techniques: Deep Q-Learning, Actor-Critic Methods, and more.

Learn from the World’s Leading Experts

Learn from the World’s Leading Experts

We collaborated with NVIDIA and Unity to build a world-class program that balances theory with practical application, and supports exploration of new approaches to compelling challenges in fields ranging from gaming to finance to robotics.

Design Your Own Algorithms
Design Your Own Algorithms

Design Your Own Algorithms

You’ll learn the theories behind the most recent advances in deep reinforcement learning, and then use that knowledge to train your own agents! You’ll complete three major projects, and build a strong portfolio in the process.

Get Personalized Project Reviews

Get Personalized Project Reviews

Get personalized feedback on your projects from a team of AI experts. Then share your polished projects on GitHub, showcasing your mastery of this advanced field.

Learn with the best

Alexis Cook
Alexis Cook

Curriculum Lead

Alexis is an applied mathematician with a Masters in Computer Science from Brown University and a Masters in Applied Mathematics from the University of Michigan. She was formerly a National Science Foundation Graduate Research Fellow.

Arpan Chakraborty
Arpan Chakraborty

Content Developer

Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.

Mat Leonard
Mat Leonard

Product Lead

Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

Luis Serrano
Luis Serrano

Content Developer

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in Mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Cezanne Camacho
Cezanne Camacho

Content Developer

Cezanne is an expert in computer vision with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied computer vision and deep learning to medical diagnostic applications.

Dana Sheahen
Dana Sheahen

Content Developer

Dana is an electrical engineer with a Masters in Computer Science from Georgia Tech. Her work experience includes software development for embedded systems in the Automotive Group at Motorola, where she was awarded a patent for an onboard operating system.

Chhavi Yadav
Chhavi Yadav

Content Developer

Chhavi is a Computer Science graduate student at New York University, where she researches machine learning algorithms. She is also an electronics engineer and has worked on wireless systems.

Juan Delgado
Juan Delgado

Content Developer

Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.

Miguel Morales
Miguel Morales

Content Developer

Miguel is a software engineer at Lockheed Martin. He earned a Masters in Computer Science at Georgia Tech and is an Instructional Associate for the Reinforcement Learning and Decision Making course. He’s the author of Grokking Deep Reinforcement Learning.

What You Will Learn

Download Syllabus
Syllabus

Deep Reinforcement Learning

Learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects.

Write your own implementations of many cutting-edge algorithms, including DQN, DDPG, and evolutionary methods.

See fewer details

4 months to complete

Prerequisite Knowledge

This program requires experience with Python, probability, machine learning, and deep learning.See detailed requirements.

  • Foundations of Reinforcement Learning

    Master the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods.

  • Value-Based Methods

    Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.

    Navigation
  • Policy-Based Methods

    Learn the theory behind evolutionary algorithms and policy-gradient methods. Design your own algorithm to train a simulated robotic arm to reach target locations.

    Continuous Control
  • Multi-Agent Reinforcement Learning

    Learn how to apply reinforcement learning methods to applications that involve multiple, interacting agents. These techniques are used in a variety of applications, such as the coordination of autonomous vehicles.

    Collaboration and Competition
Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful.
— Sebastian Thrun

Student Reviews

4.6

(27)

5 stars
20
74.1%
4 stars
4
14.8%
3 stars
2
7.4%
2 stars
1
3.7%
1 stars
0
0.0%
Markus B.

State-of-the-art program with extraordinary projects. Core of moder AI. Udacity opens new doors for people who wanr to change the world. Perfect

Junjie X.

还是可以学到很多干货的,但是要真的转行或者找工作,自己平时的努力和积累也是必不可少的

Farida H.

I loved how the research spirit was obvious in this nanodegree

Muh Alif A.

The materials are presented in a clear and elaborate way.

Robson M.

The program is very didactic, the lessons are detailed and I could learn very much with the projects. These are important adjectives because we are handling with a difficult subject.

Term 1
Deep Reinforcement Learning
$999 USD

total

Master cutting-edge deep reinforcement learning algorithms with hands-on coding exercises, and challenging, open-ended projects.

Deep Reinforcement Learning

Become a reinforcement learning expert