Multi-armed bandits thompson sampling for non-binary rewards

Question

I use the following line to update my beta distribution in each trial and give arm recommendation (I use scipy.stats.beta) :

self.prior = (1.0,1.0)
def get_recommendation(self):
    sampled_theta = []
    for i in range(self.arms):
        #Construct beta distribution for posterior
        dist = beta(self.prior[0]+self.successes[i],
                    self.prior[1]+self.trials[i]-self.successes[i])
        #Draw sample from beta distribution
        sampled_theta += [ dist.rvs() ]
    # Return the index of the sample with the largest value
    return sampled_theta.index( max(sampled_theta) )

But currently, it only works in the rewards are binary (either it's success or failure). I want to modify it so it works for non-binary rewards. (e.g. rewards: 2300, 2000,...). How do I do that?