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.
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
Value-based reinforcement learning • Stochastic policy gradients • Reinforce algorithm • Exploration-exploitation dilemma
Reinforcement learning fundamentals • 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
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.
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.
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.
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.
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.
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.
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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