This prototype combines a variational autoencoder, a mixture-density recurrent model, and a controller for CarRacing.
The components separate three parts of the problem: representing images, predicting how those representations change, and choosing an action. The training setup collects episodes and trains the components in stages.
The work is an exercise in connecting those pieces into a usable control loop. It does not establish that the learned model improves control over a direct policy.