  In algorithm optimization in reinforcement learning, how to deal with the
exploration-exploitation dilemma is particularly important. Multi-armed bandit
problem can optimize the proposed solutions by changing the reward distribution
to realize the dynamic balance between exploration and exploitation. Thompson
Sampling is a common method for solving multi-armed bandit problem and has been
used to explore data that conform to various laws. In this paper, we consider
the Thompson Sampling approach for multi-armed bandit problem, in which rewards
conform to unknown asymmetric $\alpha$-stable distributions and explore their
applications in modelling financial and wireless data.
