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Deep Reinforcement Learning

Nanodegree Program

The Deep Reinforcement Learning Nanodegree has four courses: Introduction to Deep Reinforcement Learning, Value-Based Methods, Policy-Based Methods, and Multi-Agent RL. Students learn to implement classical solution methods, define Markov decision processes, policies, and value functions, and derive Bellman equations. They learn dynamic programming, Monte Carlo methods, temporal-difference methods, deep RL, and apply these techniques to solve real-world problems. They learn to train agents to navigate virtual worlds, generate optimal financial trading strategies, and apply RL to multiple interacting agents.

The Deep Reinforcement Learning Nanodegree has four courses: Introduction to Deep Reinforcement Learning, Value-Based Methods, Policy-Based Methods, and Multi-Agent RL. Students learn to implement classical solution methods, define Markov decision processes, policies, and value functions, and derive Bellman equations. They learn dynamic programming, Monte Carlo methods, temporal-difference methods, deep RL, and apply these techniques to solve real-world problems. They learn to train agents to navigate virtual worlds, generate optimal financial trading strategies, and apply RL to multiple interacting agents.

Advanced

2 months

Real-world Projects

Completion Certificate

Last Updated November 8, 2023

Skills you'll learn:
Value-based reinforcement learning • Stochastic policy gradients • Reinforce algorithm • Exploration-exploitation dilemma
Prerequisites:
Intermediate Python • Deep learning framework proficiency • Neural network basics

Courses In This Program

Course 1 1 day

Introduction to Deep Reinforcement Learning

Course 2 4 weeks

Value Based Methods

Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.

Course 3 4 weeks

Policy-Based Methods

Course 4 3 weeks

Multi-Agent Reinforcement Learning

Taught By The Best

Photo of Mat Leonard

Mat Leonard

Content Developer

Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

Photo of Miguel Morales

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.

Photo of Chhavi Yadav

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.

Photo of Dana Sheahan

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.

Photo of Cezanne Camacho

Cezanne Camacho

Curriculum Lead

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.

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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.

Photo of Arpan Chakraborty

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.

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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.

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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.

Ratings & Reviews

Average Rating: 4.6 Stars

(328 Reviews)

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