Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Become a Deep Reinforcement Learning Expert
Nanodegree Program
Learn the deep reinforcement learning skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics.
04Days06Hrs56Min32Sec
Estimated time
4 Months
At 10-15 hrs/week
Enroll by
September 28, 2022
Get access to classroom immediately on enrollment
Prerequisites
Experience with Python, Probability, Machine Learning, & Deep Learning.
In collaboration with
What You Will Learn
Deep Reinforcement Learning
4 months to completeLearn 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.
Prerequisite knowledge
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.
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.
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.
All our programs include:
Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.
Career services
You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program offerings
Class Content
- Content Co-created with Unity
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Technical mentor support
- Student community
Career services
- Github review
- Linkedin profile optimization
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
Get timely feedback on your projects.
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Mentors available to answer your questions.
- Support for all your technical questions
- Questions answered quickly by our team of technical mentors
1,400+ technical mentors
0.85 hours median response time
Learn with the best.
Learn with the best.
Alexis Cook
Curriculum LeadAlexis 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
InstructorArpan 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
InstructorMat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
Luis Serrano
InstructorLuis 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
Curriculum LeadCezanne 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 Sheahan
Content DeveloperDana 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
Content DeveloperChhavi 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
Content DeveloperJuan 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
Content DeveloperMiguel 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.
Deep Reinforcement Learning
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Best Value
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Master cutting-edge deep reinforcement learning algorithms with hands-on coding exercises, and challenging, open-ended projects.
Average Time
On average, successful students take 3 months to complete this program.
Benefits include
- Real-world projects from industry experts
- Technical mentor support
- Career services
Program details
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