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Multiple-Strategy Based Multi-Agent Systems and Its Learning Method

基于多策略的多Agent系统及其学习方法

基本信息

批准号：
07680376
负责人：
MIKAMI Sadayoshi
金额：
$ 0.83万
依托单位：
HOKKAIDO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1996
项目状态：
已结题

项目摘要

The objective of the research is to propose a reactive planning agent that can adopt to the complex environment which usually is generated by the combination of hidden multiple dynamical factors. To this end, we have developed multi-strategic approach by independent learning agents that acquire complemental reactive strategies and then co-operate with each other to achieve global objective. The results obtained from the term of the project is summarised as follows :1. We have considered an architecture that consists of single agent containing multiple sub-agents, which have portion of input state vector and have independent performance criteria and learning mechanism. For the automatic task decomposition and arbitration of sub-agents, we have introduced a usage function for each sub-agent, which is specified by the success rate and the results of recent observations. It is shown that the usage function can be used to generate the discrimination pressure. Then by using the output of usage function as a teaching signal and the history of recent sub-agent allocation as a state, a learning arbitration module can be introduced.2. We have shown another approach by the notion of aggregate problem solving, which involves the automatic task decomposition to agents. Providing the pressure for the decomposition of knowledge, we have introduced interaction between agents through the mutual exchange of payoff function, because it is an only information that guarantees to be used in any reinforcement learning algorithm. A payoff filtering function is proposed to this end. It is shown that the function form of the filtering function should be set either competitive or co-operative according to the objective function of the whole agents. This report shows practical implementation of the method to a deadlock avoidance problem of mobile robots. The results show the effect and the promising property of the algorithm.

本研究的目的是提出一种能够适应复杂环境的反应式计划代理，这种环境通常是由隐藏的多种动力因素组合而成的。为此，我们通过独立的学习代理开发了多战略方法，这些代理获得互补的反应策略，然后相互合作以实现全球目标。项目期间取得的成果总结如下：1.我们考虑了一个由多个子智能体组成的体系结构，这些子智能体具有输入状态向量的一部分，并且具有独立的性能准则和学习机制。对于子智能体的自动任务分解和仲裁，我们为每个子智能体引入了一个使用函数，该函数由成功率和最近的观察结果来指定。结果表明，使用函数可以用来产生辨别压力。然后以使用函数的输出作为教学信号，以最近的子智能体分配历史为状态，引入学习仲裁模块。我们已经展示了另一种方法，即聚合问题解决的概念，它涉及到将任务自动分解到代理。为了提供知识分解的压力，我们通过相互交换支付函数来引入代理之间的交互，因为它是保证在任何强化学习算法中使用的唯一信息。为此，提出了收益过滤函数。结果表明，过滤函数的函数形式应根据整个智能体的目标函数设置为竞争的或合作的。本文给出了该方法在移动机器人死锁避免问题中的实际应用。实验结果表明，该算法具有较好的效果和良好的性能。