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.

OpenAI Gym is a well known RL environment/community for developing and comparing Reinforcement Learning agents.

OpenAI Gym doesn’t make assumptions about the structure of the agent and works out well with any numerical computation library such as TensorFlow, PyTorch.

The gym also provides various types of environments.

In this hands-on guide, learn how to develop a tic-tac-toe environment from scratch using OpenAI Gym.