Applying interpolation-constrained autoencoders to world models approach reinforcement learning

Applying interpolation-constrained autoencoders to world models approach reinforcement learning

World Models Approach Reinforcement Learning helps to tackle complex problems by breaking down the learning task to Vision Model, Memory Model, and Controller Model. Variational Autoencoder (VAE) is commonly used for Vision Model. However, there has been development of other variants of Autoencoders...

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Bibliographic Details
Main Author: Kevin Winata
Other Authors: Zinovi Rabinovich
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Engineering::Computer science and engineering
Online Access:https://hdl.handle.net/10356/148004
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1480042021-04-22T03:05:16Z Applying interpolation-constrained autoencoders to world models approach reinforcement learning Kevin Winata Zinovi Rabinovich School of Computer Science and Engineering Computational Intelligence Lab zinovi@ntu.edu.sg Engineering::Computer science and engineering World Models Approach Reinforcement Learning helps to tackle complex problems by breaking down the learning task to Vision Model, Memory Model, and Controller Model. Variational Autoencoder (VAE) is commonly used for Vision Model. However, there has been development of other variants of Autoencoders, with one prominent example is ACAI (Adversarially Constrained Autoencoder Interpolations). In this paper, we propose to substitute ACAI to VAE which might improve the performance on Open AI Car Racing environment. Unfortunately, the ingenuity of ACAI does not apply well to the Car Racing environment because of how ACAI is modeled. Bachelor of Engineering (Computer Science) 2021-04-22T03:05:16Z 2021-04-22T03:05:16Z 2021 Final Year Project (FYP) Kevin Winata (2021). Applying interpolation-constrained autoencoders to world models approach reinforcement learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148004 https://hdl.handle.net/10356/148004 en SCSE20-0486 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Kevin Winata
Applying interpolation-constrained autoencoders to world models approach reinforcement learning
description World Models Approach Reinforcement Learning helps to tackle complex problems by breaking down the learning task to Vision Model, Memory Model, and Controller Model. Variational Autoencoder (VAE) is commonly used for Vision Model. However, there has been development of other variants of Autoencoders, with one prominent example is ACAI (Adversarially Constrained Autoencoder Interpolations). In this paper, we propose to substitute ACAI to VAE which might improve the performance on Open AI Car Racing environment. Unfortunately, the ingenuity of ACAI does not apply well to the Car Racing environment because of how ACAI is modeled.
author2 Zinovi Rabinovich
author_facet Zinovi Rabinovich
Kevin Winata
format Final Year Project
author Kevin Winata
author_sort Kevin Winata
title Applying interpolation-constrained autoencoders to world models approach reinforcement learning
title_short Applying interpolation-constrained autoencoders to world models approach reinforcement learning
title_full Applying interpolation-constrained autoencoders to world models approach reinforcement learning
title_fullStr Applying interpolation-constrained autoencoders to world models approach reinforcement learning
title_full_unstemmed Applying interpolation-constrained autoencoders to world models approach reinforcement learning
title_sort applying interpolation-constrained autoencoders to world models approach reinforcement learning
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148004
_version_ 1698713671497154560

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