# Model-Free Deep Reinforcement Learning in Software-Defined Networks

arXiv: https://arxiv.org/abs/2209.01490 · HTML: https://mlsanity.com/paper/2209.01490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 3 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Neural Episodic Control](https://mlsanity.com/paper/1703.01988/) (2017)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [Reinforcement Learning for Autonomous Defence in Software-Defined Networking](https://mlsanity.com/paper/1808.05770/) (2018)
- [Faster Deep Q-learning using Neural Episodic Control](https://mlsanity.com/paper/1801.01968/) (2018)

## Built on by (most cited first, 1 of 1)

- [Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks](https://mlsanity.com/paper/2308.04909/) (2023)

## 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.