Skills you'll learn:
Value-Based Methods
Course
Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.
Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.
Advanced
2 weeks
Last Updated October 7, 2024
Prerequisites:
Advanced
2 weeks
Last Updated October 7, 2024
Skills you'll learn:
Prerequisites:
Course Lessons
Lesson 1
Study Plan
This lesson covers the study plan and prerequisites for this course.
Lesson 2
Deep Q-Networks
Extend value-based reinforcement learning methods to complex problems using deep neural networks.
Lesson 3 • Project
Project: Navigation
Train an agent to navigate a large world and collect yellow bananas, while avoiding blue bananas.
Taught By The Best
Dana Sheahen
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.
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.
Kelvin Lwin
AI | Knowledge Architect
Kelvin had taught in US Academia and Industry within highly technical subjects of CS and AI/DL for a decade. He expanded into building AI Fullstack in China to have a broader global perspective for 3 years. Now he is combining AI, Empathy & Ethics informed by his 18 years of meditation to build new Educational AI for all.
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