多智能体多武装强盗的公平算法
Fair Algorithms for Multi-Agent Multi-Armed Bandits
摘要：
我们提出了一个多智能体的经典多臂强盗问题的变种，其中有$N$代理和$K$武器，并拉动一个手臂产生一个（可能不同的）随机奖励每个代理。与经典的多臂强盗问题不同，目标不是学习“最好的手臂”;事实上，每个智能体都可能认为不同的手臂对她个人来说是最好的。相反，我们寻求学习武器的公平分配。借鉴经济学和计算机科学的长期研究，我们使用纳什社会福利作为我们的公平概念。我们设计了三个经典的多臂强盗算法的多智能体变体，并表明它们实现了次线性后悔，这是现在衡量的纳什社会福利损失。
英文摘要：
We propose a multi-agent variant of the classical multi-armed bandit problem, in which there are $N$ agents and $K$ arms, and pulling an arm generates a (possibly different) stochastic reward for each agent. Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm"; indeed, each agent may perceive a different arm to be the best for her personally. Instead, we seek to learn a fair distribution over the arms. Drawing on a long line of research in economics and computer science, we use the Nash social welfare as our notion of fairness. We design multi-agent variants of three classic multi-armed bandit algorithms and show that they achieve sublinear regret, which is now measured in terms of the lost Nash social welfare.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
