Become a Deep Reinforcement Learning Expert
Become a reinforcement learning expert
Enrollment Closing In
Study 10-15 hrs/week and complete in 4 months.
Classroom opens 7 days after enrollment closes
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.
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.
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.
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 feedback on your projects from a team of AI experts. Then share your polished projects on GitHub, showcasing your mastery of this advanced field.
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 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 is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
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 is a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine learning to medical diagnostic applications.
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 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 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 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.
This program requires experience with Python, probability, machine learning, and deep learning.See detailed requirements.
Master the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods.
Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.Navigation
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
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
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
I loved how the research spirit was obvious in this nanodegree
The materials are presented in a clear and elaborate way.
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.
The demand for engineers with reinforcement learning and deep learning skills far exceeds the number of engineers with these skills. This program offers a unique opportunity for you to develop these in-demand skills. You’ll implement several deep reinforcement learning algorithms using a combination of Python and deep learning libraries that will serve as portfolio pieces to demonstrate the skills you’ve acquired. As interest and investment in this space continues to increase, you’ll be ideally positioned to emerge as a leader in this groundbreaking field.
This program is designed to build on your existing skills in machine learning and deep learning. As such, it doesn't prepare you for a specific job, but instead expands your skills in the deep reinforcement learning domain. These skills can be applied to various applications such as gaming, robotics, recommendation systems, autonomous vehicles, financial trading, and more.
This program offers an ideal path into the world of deep reinforcement learning—a transformational technology that is reshaping our future, and driving amazing new innovations in Artificial Intelligence. If you're interested in applying AI to fields such as gaming, robotics, autonomous systems, and financial trading, this is the perfect way to get started.
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
We recommend that you complete a course in Deep Learning equivalent to the Deep Learning Nanodegree program prior to entering the program. You will need to be able to communicate fluently and professionally in written and spoken English.
Additionally, you should have the following knowledge:
Basic shell scripting:
Basic statistical knowledge, including:
Intermediate differential calculus and linear algebra, including:
We have a number of Nanodegree programs and free courses that can help you prepare, including:
The Deep Reinforcement Learning Nanodegree program consists of one four-month long term. All students must successfully complete three projects in order to graduate. Each project will be reviewed by one of the project reviewers in the Udacity reviewer network. Your reviewer will give you detailed feedback on your work and let you know where your project needs improvement, if necessary. If you do not pass the project, you will be asked to submit again until you pass in order to successfully complete the term.
Please see the Udacity Nanodegree program FAQs found here for policies on enrollment in our programs.
You will need a computer running a 64-bit operating system (most modern Windows, OS X, and Linux versions will work) with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.6 and supporting packages. Your network should allow secure connections to remote hosts (like SSH). We will provide you with instructions to install the required software packages.