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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.

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00Days13Hrs58Min16Sec

  • Estimated time
    4 Months

    At 10-15 hrs/week

  • Enroll by
    August 17, 2022

    Get access to classroom immediately on enrollment

  • Prerequisites
    Experience with Python, Probability, Machine Learning, & Deep Learning.
In collaboration with
  • Unity
  • Nvidia Deep Learning Institute

What You Will Learn

  1. Deep Reinforcement Learning

    4 months to complete

    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.

    Prerequisite knowledge

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

    1. 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:

    • 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.

    • 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 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

      Instructor

      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

      Instructor

      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

      Instructor

      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

      Curriculum Lead

      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 Sheahan

      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

      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

      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

      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.

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    Deep Reinforcement Learning

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      Best Value
    • Learn

      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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    Deep Reinforcement Learning

    Become a reinforcement learning expert

    Enroll now