Model-based Variational Autoencoders with Autoregressive Flows

摘要

Variational autoencoders are employed to provide a framework for learning deep latent state representation. Inverse autoregressive flow is a type of normalizing flow that is employed to provide strategies for flexible variational inferences of posteriors over latent variables. The study aimed to prove that the agent can find a solution faster and at a lower cost. The proposed architecture comprises three basic methods, whereby the first one initiates the parameters and other layers of the TensorFlow framework; the second one is the build method that develops a layer using the Kera Library, and the last method, transform, determines the next sequence in the chain and changes the input. The model was then tested on a car racing simulator from OpenAI Gym. It was concluded that the proposed model is fast because it achieved a score of 928 ± 14 over 100 random trials, which is the best in the tested environment.