Become a Deep Reinforcement Learning Expert

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:

• Intermediate Python programming knowledge, including:
  • Strings, numbers, and variables
  • Statements, operators, and expressions
  • Lists, tuples, and dictionaries
  • Conditions, loops
  • Generators & comprehensions
  • Procedures, objects, modules, and libraries
  • Troubleshooting and debugging
  • Research & documentation
  • Problem solving
  • Algorithms and data structures

Basic shell scripting:

• Run programs from a command line
• Debug error messages and feedback
• Set environment variables
• Establish remote connections

Basic statistical knowledge, including:

• Populations, samples
• Mean, median, mode
• Standard error
• Variation, standard deviations
• Normal distribution

Intermediate differential calculus and linear algebra, including:

• Derivatives & Integrals
• Series expansions
• Matrix operations through eigenvectors and eigenvalues

We have a number of Nanodegree programs and free courses that can help you prepare, including:

• Intro to Machine Learning
• Artificial Intelligence Programming with Python Nanodegree program
• Machine Learning Engineer Nanodegree program
• Deep Learning Nanodegree program

The Deep Reinforcement Learning Nanodegree program is comprised of content and curriculum to support three (3) projects. We estimate that students can complete the program in four (4) months working 10 hours per week.

Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.

Access to this Nanodegree program runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our Nanodegree programs.

Please see the Udacity Program FAQs 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.