A #worldmodel is generally viewed as "an abstract representation of the spatial or temporal dimensions of our world". Humans develop a mental model of the world based on what they are able to perceive with their limited senses. The decisions and actions we make are based on this internal model and what we perceive at any given moment is governed by our brain's prediction of the future based on our internal model. This guides model-based ML NNs models aimed to learn condensed/compressed spatial and temporal representations of data for real-life interactions with the environment, like Vision (V), Memory (M), and Controller (C) VAE (V) agent model. The role of the V model is to learn an abstract, compressed representation of each observed input frame, the role of the M model is to predict the future.