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Thompson sampling has become a ubiquitous approach to online decisionproblems with bandit feedback. The key algorithmic task for Thompson samplingis drawing a sample from the posterior of the optimal action. We propose analternative arm selection rule we dub TS-UCB, that requires negligibleadditional computational effort but provides significant performanceimprovements relative to Thompson sampling. At each step, TS-UCB computes ascore for each arm using two ingredients: posterior sample(s) and upperconfidence bounds. TS-UCB can be used in any setting where these two quantitiesare available, and it is flexible in the number of posterior samples it takesas input. TS-UCB achieves materially lower regret on a comprehensive suite ofsynthetic and real-world datasets, including a personalized articlerecommendation dataset from Yahoo! and a suite of benchmark datasets from adeep bandit suite proposed in Riquelme et al. (2018). Finally, from atheoretical perspective, we establish optimal regret guarantees for TS-UCB forboth the K-armed and linear bandit models.

TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation