What are the five steps involved in the training process of the controller in the World Models framework?

The five steps involved in the training process of the controller in the World Models framework are: 1) Collect random rollout data, 2) Train the VAE, 3) Collect data to train the MDN-RNN, 4) Train the MDN-RNN, and 5) Train the controller using CMA-ES.

The training process of the controller in the World Models framework involves a sequence of five steps. First, random rollout data is collected by having the agent explore the environment with random actions, which helps in understanding the environment's physics. Second, a Variational Autoencoder (VAE) is trained on the collected observation images to encode them into a latent space. Third, data is collected to train the Mixture Density Network-Recurrent Neural Network (MDN-RNN) by encoding observations into latent vectors using the trained VAE. Fourth, the MDN-RNN is trained to predict the evolution of these latent vectors over time. Finally, the controller is trained using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to optimize actions based on the current latent state and hidden state of the RNN.