Study 10-15 hrs/week and complete in 4 months.
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 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 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.
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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
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 projects are very useful to know about deep reinforcement learning.
Program is generally OK. However, the multi-agent reinforcement learning component has been disappointing. I think the content on that section can be improved significantly.
Excellent first part (intro to DL, value-based methods). Policy based and multi-agent were much lower quality - no supporting materials (e.g., cheatsheet, video notes, quizzes)
Master cutting-edge deep reinforcement learning algorithms with hands-on coding exercises, and challenging, open-ended projects.