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

Date

2020-04-01

Author

Aydın, Ayberk
Sürer, Elif

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Deep Reinforcement Learning (DRL) has been suc-cessfully applied in several research domains such as robotnavigation and automated video game playing. However, thesemethods require excessive computation and interaction with theenvironment, so enhancements on sample efficiency are required.The main reason for this requirement is that sparse and delayedrewards do not provide an effective supervision for representationlearning of deep neural networks. In this study, Proximal PolicyOptimization (PPO) algorithm is augmented with GenerativeAdversarial Networks (GANs) to increase the sample efficiency byenforcing the network to learn efficient representations withoutdepending on sparse and delayed rewards as supervision. Theresults show that an increased performance can be obtained byjointly training a DRL agent with a GAN discriminator.

Subject Keywords

Deep learning, Reinforcement learning, Genera-tive adversarial networks

URI

https://arxiv.org/abs/2004.02762
https://hdl.handle.net/11511/79368

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Unverified, Article

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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,” 2020, Accessed: 00, 2021. [Online]. Available: https://arxiv.org/abs/2004.02762.