def eps_greedy(self, params):
        '''
        Run the epsilon-greedy strategy and update self.max_mean()

        Parameters
        ----------
        Params : dict
            Epsilon

        Returns
        -------
        int
            Index of chosen bandit
        '''

        if params and type(params) == dict:
            eps = params.get('epsilon')
        else:
            eps = 0.1

        r = np.random.rand()

        if r < eps:
            return np.random.choice(list(set(range(len(self.wins))) -
                                    {self.max_mean()}))
        else:
            return self.max_mean()