Machine Learning with Phil has got another interesting look at Deep Q Learning as part of a preview of his course.

The two biggest innovations in deep Q learning were the introduction of the target network and the replay memory. One would think that simply bolting a deep neural network to the Q learning algorithm would be enough for a robust deep Q learning agent, but that isn’t the case. In this video I’ll show you how this naive implementation of the deep q learning agent fails, and spectacularly at that.

This is an excerpt from my new course, Deep Q Learning From Paper to Code which you can get on sale with this link

https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=CYBERMONDAY19

After a particularly fascinating talk I attended last week at MLADS, I want to spend more time focused on Deep Q Learning.

Fortunately, YouTuber Phil has just created a course on Udemy about Deep Q Learning.

https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?referralCode=CBA45A3B737237E7BFD2

Github for the course is here:

https://github.com/philtabor/Deep-Q-Learning-Paper-To-Code

 

Website: https://www.neuralnet.ai
Course: https://www.manning.com/livevideo/reinforcement-learning-in-motion
Github: https://github.com/philtabor
Twitter: https://twitter.com/MLWithPhil