As we saw in previous chapters, reinforcement learning demonstrates insufficient adaptability to high-dimensional input data. This problem is overcome by using low-dimensional characteristics vectors to represent high-dimensional input. However, finding useful vectors of features can be complicated, as it requires a good understanding of the problem.
One way to change the dimensionality of data is the autoencoder. Autoencoders are artificial neural networks with a hidden layer, which has the desired dimensionality of the input data; both input and output levels have the same amount of units. In these models, the network is trained to reproduce the input values ​​in the output level. As we saw in the previous section, the autoencoder learns two functions: an encoder function and a decoder function.
During reinforcement learning, the amount...
