Thompson Sampling on Asymmetric $\alpha$-Stable Bandits

• URL: http://arxiv.org/abs/2203.10214v1
• Date: Sat, 19 Mar 2022 01:55:08 GMT
• Title: Thompson Sampling on Asymmetric $\alpha$-Stable Bandits
• Authors: Zhendong Shi
• Abstract summary: Multi-armed bandit problem can optimize the proposed solutions by changing the reward distribution. Thompson Sampling is a common method for solving multi-armed bandit problem.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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