Value Based Methods

Course

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

Skills

Value-based reinforcement learning

Prioritized experience replay

Deep q-networks

Double deep q-networks

Advanced

4 weeks

Real-world Projects

Completion Certificate

Last Updated August 11, 2023

Prerequisites:

Reinforcement learning fundamentals

Deep learning framework proficiency

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.