Now that we understand the reinforcement learning process in detail, we can look to adapt our Q-learning model to work with deep learning. This, as you could likely guess, is the culmination of our efforts and where the true power of RL shines. As we learned through earlier chapters, deep learning is essentially a complex system of equations that can map inputs through a non-linear function to generate a trained output.
A neural network is just another, simpler method of solving a non-linear equation. We will look at how to use DNN to solve other equations later, but for now we will focus on using it to solve the Q-learning equation we saw in the previous section.
We will use the CartPole training environment from the OpenAI Gym toolkit. This environment is pretty much the standard used to learn Deep Q-learning (DQN).
Open up Chapter_5_4.py and...
