Model-Free Deep Reinforcement Learning in Software-Defined Networks
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Abstract (arXiv)
This paper compares two deep reinforcement learning approaches for cyber security in software defined networking. Neural Episodic Control to Deep Q-Network has been implemented and compared with that of Double Deep Q-Networks. The two algorithms are implemented in a format similar to that of a zero-sum game. A two-tailed T-test analysis is done on the two game results containing the amount of turns taken for the defender to win. Another comparison is done on the game scores of the agents in the respective games. The analysis is done to determine which algorithm is the best in game performer and whether there is a significant difference between them, demonstrating if one would have greater preference over the other. It was found that there is no significant statistical difference between the two approaches.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 3 Sep 2022.
Builds on
6 of 13 references.
References · 6
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
- 2017Neural Episodic Controlcited by 148
- 2019Deep Reinforcement Learning for Cyber Securitycited by 92
- 2018Reinforcement Learning for Autonomous Defence in Software-Defined Networkingcited by 17
- 2018Faster Deep Q-learning using Neural Episodic Controlcited by 4
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Citing papers per year: 2023: 1.
1 paper.