Transfer in Sequential Multi-Armed Bandits via Reward Samples

We consider a sequential stochastic multi-armed bandit problem where the agent interacts with the bandit over multiple episodes. The reward distribution of the arms remains constant throughout an episode but can change over different episodes. We propose an algorithm based on UCB to transfer the reward samples from the previous episodes and improve the cumulative regret performance over all the episodes. We provide regret analysis and empirical results for our algorithm, which show significant improvement over the standard UCB algorithm without transfer.