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A quick, and perhaps over-simplified answer to your question, based on my own understanding:
We need to make a model that creates a simplified/condensed representation of the image of the car, so the PPO agent can make a decision and not take too long to train, as raw images simply contain too much data and are too specific. The VAE is one method to create a latent representation of what the car is seeing, and passes that condensed vector to the PPO for its decision making. One could, for example, swap that out for a CNN or anything else that can learn latent variables regarding an image, but its not likely to work nearly as well if you put raw images into the PPO agent.
Hi,
When I run it, I have some questions:
Why is a Variational Autoencoder (VAE) network needed? Is it necessary, or can it be removed?
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