arXiv:2501.04403cs.LG2025-01被引 2
提出线性漂移下的多臂赌博机算法，实现更优的性能上限。
Rising Rested MAB with Linear Drift
- 基于动作执行次数的线性漂移建模，设计新策略应对非平稳环境
- 理论证明后悔上界为 $\tildeΘ(T^{4/5}K^{3/5})$，上下界一致
- 适用于需精准刻画奖励变化规律的动态决策场景
我们研究非平稳多臂赌博机问题，其中每种动作的期望回报随其被执行次数呈线性变化。主要成果是给出了紧致的后悔上界 $\tildeΘ(T^{4/5}K^{3/5})$，同时提供相应的下界证明。进一步扩展结果，推导出依赖于未知奖励漂移参数实例的后悔上界。
原文摘要 · Abstract (English)
We consider non-stationary multi-arm bandit (MAB) where the expected reward of each action follows a linear function of the number of times we executed the action. Our main result is a tight regret bound of $\tildeΘ(T^{4/5}K^{3/5})$, by providing both upper and lower bounds. We extend our results to derive instance dependent regret bounds, which depend on the unknown parametrization of the linear drift of the rewards.
多臂赌博机在线学习后悔分析
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。