A$. Two techniques are widely used in this framework: $Q$-learning and policy learning.

In $Q$-learning, a function $Q$ is defined that takes as input one state and one action and returns the expected reward of that action and all subsequent actions at that state. Before learning begins, $Q$ is initialized to a random fixed value and it gets updated as the learning proceeds until it converges to an optimal value.

In policy learning, a mapping $\pi: S \rightarrow A$ is introduced from the set of states to the set of actions. We refer to this map as policy: $a=\pi(s)$ means that when state $s$ is observed, the best thing to do is to take action $a$. The task then becomes to find a policy with maximum expected return.

Adversarial Machine Learning
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Adversarial Machine Learning (AML) is a research field that lies at the intersection of machine learning and computer security. Here, we present a general categorization of attacks in the context of machine learning. Below we classify possible attacks into three main pillars.

First, the timing of the attack plays a crucial role in the process. The main distinction occurs between evasion attacks and poisoning attacks. The former ones 