An explore-then-commit algorithm for submodular maximization under full-bandit feedback
An explore-then-commit algorithm for submodular maximization under full-bandit feedback
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发表时间：
2022
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通讯作者：
G. Nie;Mridul Agarwal;A. Umrawal;V. Aggarwal;Christopher J. Quinn
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作者：
G. Nie;Mridul Agarwal;A. Umrawal;V. Aggarwal;Christopher J. Quinn
我们研究了与随机的（在预期的）奖励和全班态反馈中的组合匪徒的问题，除了每个时间步骤t的选定动作的奖励以外，没有其他信息，我们提出了一个简单的算法，探索了一个范围。地平线T，基本数量元素n和基数约束k。
We investigate the problem of combinatorial multiarmed bandits with stochastic submodular (in expectation) rewards and full-bandit feedback, where no extra information other than the reward of selected action at each time step t is ob-served. We propose a simple algorithm, Explore-Then-Commit Greedy (ETCG) and prove that it achieves a (1 − 1 /e ) -regret upper bound of O ( n 13 k 43 T 23 log( T ) 12 ) for a horizon T , number of base elements n , and cardinality constraint k . We also show in experiments with synthetic and real-world data that the ETCG empirically outperforms other full-bandit methods.