论文标题
随机代理网络中以声誉驱动的决策
Reputation-driven Decision-making in Networks of Stochastic Agents
论文作者
论文摘要
本文研究了涉及自我利用代理网络的多代理系统。我们提出了一个Markov决策过程衍生的框架，称为Repnet-MDP，该框架是根据域名定制的，在该域中，代理声誉是代理之间交互的关键驱动力。基本面基于Repnet-Pomdp的原理，Rens等人开发的框架。在2018年，但仅考虑完全可观察到的环境来解决其数学上的不一致性，并减轻了其棘手性。此外，我们还使用一种在线学习算法来查找Repnet MDP的近似解决方案。在一系列实验中，依次剂被证明能够使自己的行为适应网络其余代理的过去行为和可靠性。最后，我们的工作确定了当前表述中该框架的局限性，该框架阻止了其代理人在不是主要演员的情况下学习的情况。
This paper studies multi-agent systems that involve networks of self-interested agents. We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domains in which agent reputation is a key driver of the interactions between agents. The fundamentals are based on the principles of RepNet-POMDP, a framework developed by Rens et al. in 2018, but addresses its mathematical inconsistencies and alleviates its intractability by only considering fully observable environments. We furthermore use an online learning algorithm for finding approximate solutions to RepNet-MDPs. In a series of experiments, RepNet agents are shown to be able to adapt their own behavior to the past behavior and reliability of the remaining agents of the network. Finally, our work identifies a limitation of the framework in its current formulation that prevents its agents from learning in circumstances in which they are not a primary actor.
