Very good multi K-Armed Bandits solver, no need parameter!

RandomUpperConfidenceInterval.py

class RandomUCI():
    """Random Upper Confidence Interval
    """
    def __init__(self, bandit):
        self.bandit = bandit
        self.arm_count = bandit.arm_count
        self.mean = np.ones(self.arm_count) / 2
        self.variance = np.ones(self.arm_count) / 4
        self.N = np.ones(self.arm_count)

    @staticmethod
    def name():
        return 'Random-UCI'

    def get_reward_regret(self, arm):
        reward, regret = self.bandit.get_reward_regret(arm)
        self._update_params(arm, reward)
        return reward, regret

    def get_action(self):
        return np.argmax(self.mean + np.random.random() * np.sqrt(self.variance) / self.N)

    def _update_params(self, arm, reward):
        self.N[arm] += 1
        delta = reward - self.mean[arm]
        self.mean[arm] += delta / self.N[arm]
        self.variance[arm] += delta * (reward - self.mean[arm])