def prob(self, s, terminal, p_actions):
        p = np.ones(len(p_actions)) / len(p_actions)
        p *= self.epsilon
        b_actions = self.representation.bestActions(s, terminal, p_actions)
        if self.forcedDeterministicAmongBestActions:
            p[b_actions[0]] += (1 - self.epsilon)
        else:
            p[b_actions] += (1 - self.epsilon) / len(b_actions)
        return p
dataset/ETHPy150Open rlpy/rlpy/rlpy/Policies/eGreedy.py/eGreedy.prob/original