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Efficient Deep Reinforcement Learning by Combining Variational Autoencoders with Soft Actor Critic

Moritz Nekolla

Informationen zur Arbeit

Abschlussarbeitstyp: Bachelor
Betreuer: Mohammd Karam Daaboul
Forschungsgruppe: Angewandte Technisch-Kognitive Systeme
Partner: FZI
Archivierungsnummer: 4679
Abschlussarbeitsstatus: Abgeschlossen
Beginn: 01. August 2020
Abgabe: unbekannt

Weitere Informationen

The objective of this work is to develop and evaluate the power of a Variational Autoencoder (VAE) to enforce Reinforcement Learning (RL) on a wide variety of tasks. This should be enabled by automatically extracting important features from the observation and creating a more dense representation. Applying an RL algorithm on this pre-trained latent space could be more likely to converge.