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Q-learning starts with a random function $\mathcal{Q}$ and repeatedly takes actions and then updates $\mathcal{Q}$ with the observed reward. Actions are taken stochastically. The preference given to actions promising a high reward (according to the current state of $\mathcal{Q}$) is equivalent to the preference of exploitation over exploration. Another parameter of Q-learning is the learning rate which determines how strongly each observed reward changes the $\mathcal{Q}$ function in the next step.