Incentivized Exploration for Multi-Armed Bandits under Reward Drift

12 Nov 2019Zhiyuan LiuHuazheng WangFan ShenKai LiuLijun Chen

We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward. We seek to understand the impact of this drifted reward feedback by analyzing the performance of three instantiations of the incentivized MAB algorithm: UCB, $\varepsilon$-Greedy, and Thompson Sampling... (read more)

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