Hands-On Artificial Intelligence for Cybersecurity

Reinforcement learning

In the case of reinforcement learning (RL), a different learning strategy is followed, which emulates the trial and error approach. Thus, drawing information from the feedback obtained during the learning path, with the aim of maximizing the reward finally obtained based on the number of correct decisions that the algorithm has selected.

In practice, the learning process takes place in an unsupervised manner, with the particularity that a positive reward is assigned to each correct decision (and a negative reward for incorrect decisions) taken at each step of the learning path. At the end of the learning process, the decisions of the algorithm are reassessed based on the final reward achieved.

Given its dynamic nature, ...