def select_action(self, q_values):
        """Return the selected action
        The selected action follows the BoltzmannQPolicy with probability epsilon
        or return the Greedy Policy with probability (1 - epsilon)

        # Arguments
            q_values (np.ndarray): List of the estimations of Q for each action

        # Returns
            Selection action
        """
        assert q_values.ndim == 1
        q_values = q_values.astype('float64')
        nb_actions = q_values.shape[0]

        if np.random.uniform() < self.eps:
            exp_values = np.exp(np.clip(q_values / self.tau, self.clip[0], self.clip[1]))
            probs = exp_values / np.sum(exp_values)
            action = np.random.choice(range(nb_actions), p=probs)
        else:
            action = np.argmax(q_values)
        return action