Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning

Date

2020-10-05

Author

Aydın, Ayberk
Sürer, Elif

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Deep Reinforcement Learning (DRL) has been successfully applied in several research domains such as robot navigation and automated video game playing. However, these methods require excessive computation and interaction with the environment, so enhancements on sample efficiency are required. The main reason for this requirement is that sparse and delayed rewards do not provide an effective supervision for representation learning of deep neural networks. In this study, Proximal Policy Optimization (PPO) algorithm is augmented with Generative Adversarial Networks (GANs) to increase the sample efficiency by enforcing the network to learn efficient representations without depending on sparse and delayed rewards as supervision. The results show that an increased performance can be obtained by jointly training a DRL agent with a GAN discriminator.

URI

https://ieeexplore.ieee.org/abstract/document/9302454
https://hdl.handle.net/11511/93337

Conference Name

2020 28th Signal Processing and Communications Applications Conference (SIU)

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Graduate School of Informatics, Conference / Seminar

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Citation Formats

A. Aydın and E. Sürer, “Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning,” presented at the 2020 28th Signal Processing and Communications Applications Conference (SIU), İstanbul, Türkiye, 2020, Accessed: 00, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9302454.