As we said in the Deep Q-learning section, Deep Q-learning identifies a reinforcement learning method of the approximation of a function. It therefore represents an evolution of the basic Q-learning method since the state-action table is replaced by a neural network, with the aim of approximating the optimal value function.
Compared to the previous approaches, where it was used to structure the network in order to request both input and action, and provided its expected return, Deep Q-learning revolutionizes the structure to request only the state of the environment and supply as many status-action values as there are actions that can be performed in the environment.
In the following code, a Deep Q-learning solution ...
