![reinforcement learning-Q learning [18]. | Download Scientific Diagram](https://www.researchgate.net/profile/B-P-Patil/publication/321386194/figure/fig1/AS:722248193306624@1549208796730/reinforcement-learning-approach_Q320.jpg)
%20was%20used%20as%20an%20environment%20to%20train%20reinforcement%20learning%20agents%20to%20solve%20three%20predefined%20security%20scenarios.%20These%20scenarios%20cover%20techniques%20of%20exploitation%2C%20post-exploitation%20and%20wiretapping.%20A%20large%20hyperparameter%20grid%20search%20was%20performed%20to%20find%20the%20best%20hyperparameter%20combinations.%20The%20algorithms%20Q-learning%2C%20DQN%20and%20A3C%20were%20used%2C%20whereby%20A3C%20was%20able%20to%20solve%20all%20scenarios%20and%20achieve%20generalization.%20In%20addition%2C%20A3C%20could%20solve%20these%20scenarios%20with%20fewer%20actions%20than%20the%20baseline%20automated%20penetration%20testing.%20Although%20the%20training%20was%20performed%20on%20rather%20small%20scenarios%20and%20with%20small%20state%20and%20action%20spaces%20for%20the%20agents%2C%20the%20results%20show%20that%20a%20penetration%20test%20can%20successfully%20be%20performed%20by%20the%20RL%20agent.)
![Reinforcement Learning [Part 2]: The Q-learning Algorithm | HackerNoon](https://hackernoon.com/images/DUgalx4alqOf4QlthE3dDI1x7Wq2-tkd3lix.png)
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