Reinforcement Learning Algorithms with Python

Building a model from images

The methods seen so far that combine model-based and model-free learning have been designed especially to work with low-dimensional state spaces. So, how do we deal with high-dimensional observation spaces as images?

One choice is to learn in latent space. Latent space is a low-dimensional representation, also called embedding, g(s), of a high-dimensional input, s, such as an image. It can be produced by neural networks such as autoencoders. An example of an autoencoder is shown in the following diagram:

It comprises an encoder that maps the image to a small latent space, g(s), and the decoder that maps the latent ...