论文标题
池塘：悲观的在线派遣
POND: Pessimistic-Optimistic oNline Dispatching
论文作者
论文摘要
本文考虑到未知的到达，奖励和约束分布，考虑了受限的在线派遣。我们提出了一种新颖的在线派遣算法，名为Pond，代表悲观的在线派遣，可以实现$ o（\ sqrt {t}）$遗憾和$ o（1）$约束违规。两个边界都是锋利的。我们对合成和真实数据集的实验表明，池塘对违规的最小限制造成了较低的遗憾。
This paper considers constrained online dispatching with unknown arrival, reward and constraint distributions. We propose a novel online dispatching algorithm, named POND, standing for Pessimistic-Optimistic oNline Dispatching, which achieves $O(\sqrt{T})$ regret and $O(1)$ constraint violation. Both bounds are sharp. Our experiments on synthetic and real datasets show that POND achieves low regret with minimal constraint violations.
