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Fuzzy Deep Reinforcement Learning for autoscaling on clouds

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# Fuzzy Deep Reinforcement Learning

Inspired by the recent variants in Deep RL, Fuzzy Deep RL is developed by using Fuzzy Logic as a data representation method and Deep Q-Networks for autoscaling problem

### References:
The used dataset from Clarknet Traces <ftp://ita.ee.lbl.gov/traces/>

Simulation code refined from [Feedback Control for Computer Systems by Philipp K. Janert (O'Reilly Media)](https://github.com/oreillymedia/feedback_control_for_computer_systems)

Fuzzy Q-Learning corrected from [FQL](https://github.com/seyedsaeidmasoumzadeh/Fuzzy-Q-Learning)

Deep QN architecture derived from [Hands-on Deep Reinforcement Learning, published by Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On)

For the use of fuzzy logic with DQN, please see [Fuzzy DQN](https://github.com/doandongnguyen/FuzzyDQN) and Fuzzy Q Learning [FQL](https://github.com/doandongnguyen/FQL-Fuzzy-Q-Learning)

If you use this code please cite as:

Doan D.N., Zaharie D., Petcu D. (2020) Auto-scaling for a Streaming Architecture with Fuzzy Deep Reinforcement Learning. In: Schwardmann U. et al. (eds) Euro-Par 2019: Parallel Processing Workshops. Euro-Par 2019. Lecture Notes in Computer Science, vol 11997. Springer, Cham. https://doi.org/10.1007/978-3-030-48340-1_37

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