Machine Learning with Phil has a great tutorial on how to do Deep Q Learning in PyTorch.

The PyTorch deep learning framework makes coding a deep q learning agent in python easier than ever. We’re going to code up the simplest possible deep Q learning agent, and show that we only need a replay memory to get some serious results in the Lunar Lander environment from the Open AI Gym. We don’t really need the target network, though it has been known to help the deep Q learning algorithm with convergence.

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