Learning Fairness under Constraints: A Decentralized Resource Allocation Game
Learning Fairness under Constraints: A Decentralized Resource Allocation Game
复制标题
DOI：
10.1109/icmla.2016.0043
复制
发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Qinyun Zhu;J. Oh
中科院分区：
文献类型：
--
作者：
Qinyun Zhu;J. Oh
本文研究了多Agent系统中的多类型资源分配问题，其中对资源的提供者和使用者都施加了一定的约束。这些约束是资源类型和连接可用性的限制，这可能会使代理之间的协作不可行。我们讨论了这些约束下分布式资源公平性的概念。然后提出了一种基于博弈论和强化学习的协同资源分配方案，使资源公平地分配给用户，任务有效地分配给资源代理。我们利用来自Google数据中心的数据作为模拟的输入。结果表明，我们的学习方法优于贪婪和随机探索的资源利用率和公平性。
We study multi-type resource allocation in multi-agent system, where some constraints are enforced upon resource providers and users. These constraints are limitations of resource types and connection availabilities, which may make the collaboration between agents infeasible. We discuss the notion of distributed resource fairness under these constraints. Then we propose a game theory and reinforcement learning based solution for collaborative resource allocation, so that resources are assigned to users fairly and tasks are assigned to resource agents efficiently. We utilize data from Google data center as our input to simulations. Results show that our learning approach outperforms a greedy and random explorations in terms of resource utilization and fairness.