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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| 格式: | Final Year Project |
| 語言: | English |
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Nanyang Technological University
2021
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/148004 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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. |
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