解决多峰奖励的老虎机问题，设计了首个高效可行的最优算法。
Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
- 基于格雷夫斯-莱优化问题，提出可计算的近似解法
- 实现渐近最优，理论后悔下界与实际表现接近
- 适合需要高精度决策的多峰环境应用
我们研究一种独立同分布奖励的随机多臂老虎机问题，其期望奖励函数最多有 m 个模式。提出首个已知的计算上可行的算法来求解格雷夫斯-莱优化问题，从而实现该问题的渐近最优算法。所提算法的代码已在 https://github.com/wilrev/MultimodalBandits 公开。
原文摘要 · Abstract (English)
We consider a stochastic multi-armed bandit problem with i.i.d. rewards where the expected reward function is multimodal with at most m modes. We propose the first known computationally tractable algorithm for computing the solution to the Graves-Lai optimization problem, which in turn enables the implementation of asymptotically optimal algorithms for this bandit problem. The code for the proposed algorithms is publicly available at https://github.com/wilrev/MultimodalBandits
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