基于收益的非均匀部分非理性博弈在多智能体系统势博弈协同控制中的应用
Payoff-based Inhomogeneous Partially Irrational Play for Potential Game Theoretic Cooperative Control of Multi-agent Systems
AI总结：
提出一种基于收益的非均匀部分非理性博弈算法，通过允许智能体以一定概率做出非理性决策，证明集体行动以概率收敛于势函数最大值，并在传感器覆盖实验中验证了算法在存在不良纳什均衡和环境变化时的有效性。
摘要：
本文处理一类称为势博弈的博弈策略，并开发了一种新颖的学习算法——基于收益的非均匀部分非理性博弈（PIPIP）。该算法基于现有工作中提出的分布式非均匀同步学习（DISL），但与DISL不同，PIPIP允许智能体以指定概率做出非理性决策，即智能体可以从存储在记忆中的过去动作中选择一个低效用的动作。由于非理性决策，我们可以证明集体行动以概率收敛于势函数最大值。最后，我们通过传感器覆盖问题的实验展示了本算法的有效性。实验表明，即使在存在不良纳什均衡的情况下，本学习算法也能成功引导智能体接近势函数最大值。我们还通过移动密度函数的实验看到，PIPIP对环境变化具有适应性。
英文摘要：
This paper handles a kind of strategic game called potential games and develops a novel learning algorithm Payoff-based Inhomogeneous Partially Irrational Play (PIPIP). The present algorithm is based on Distributed Inhomogeneous Synchronous Learning (DISL) presented in an existing work but, unlike DISL,PIPIP allows agents to make irrational decisions with a specified probability, i.e. agents can choose an action with a low utility from the past actions stored in the memory. Due to the irrational decisions, we can prove convergence in probability of collective actions to potential function maximizers. Finally, we demonstrate the effectiveness of the present algorithm through experiments on a sensor coverage problem. It is revealed through the demonstration that the present learning algorithm successfully leads agents to around potential function maximizers even in the presence of undesirable Nash equilibria. We also see through the experiment with a moving density function that PIPIP has adaptability to environmental changes.
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。
