  We consider a sequential stochastic multi-armed bandit problem where the
agent interacts with bandit over multiple episodes. The reward distribution of
the arms remain 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.
