Machine Learning with Phil dives into Deep Q Learning with Tensorflow 2 and Keras.

Dueling Deep Q Learning is easier than ever with Tensorflow 2 and Keras. In this tutorial for deep reinforcement learning beginners we’ll code up the dueling deep q network and agent from scratch, with no prior experience needed. We’ll train an agent to land a spacecraft on the surface of the moon, using the lunar lander environment from the OpenAI Gym.

The dueling network can be applied to both regular and double q learning, as it’s just a new network architecture. It doesn’t require any change to the q learning or double q learning algorithms. We simply have to change up our feed forward to accommodate the new value and advantage streams, and combine them in a way that makes sense.

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.

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.

Github for the course is here: