论文标题
合作多机构学习中强大的事件驱动互动
Robust Event-Driven Interactions in Cooperative Multi-Agent Learning
论文作者
论文摘要
我们提出了一种方法，以利用基础马尔可夫决策过程的固有鲁棒性来减少多代理学习系统中所需的通信。我们计算所谓的鲁棒性替代功能（离线），这使代理人保守地表明其状态测量在需要更新系统中的其他代理之前可能会偏离多远。这导致了完全分布的决策功能，使代理可以决定何时需要更新他人。我们根据获得的奖励总和来得出所得系统的最优性，并显示这些界限是设计参数的函数。此外，我们扩展了从数据中学到鲁棒性替代功能的情况下的结果，并提出了实验结果，证明了代理之间的通信事件显着降低。
We present an approach to reduce the communication required between agents in a Multi-Agent learning system by exploiting the inherent robustness of the underlying Markov Decision Process. We compute so-called robustness surrogate functions (off-line), that give agents a conservative indication of how far their state measurements can deviate before they need to update other agents in the system. This results in fully distributed decision functions, enabling agents to decide when it is necessary to update others. We derive bounds on the optimality of the resulting systems in terms of the discounted sum of rewards obtained, and show these bounds are a function of the design parameters. Additionally, we extend the results for the case where the robustness surrogate functions are learned from data, and present experimental results demonstrating a significant reduction in communication events between agents.
