基于专家度的模糊自适应合作学习算法,在羊群问题中的应用

M. Akbarzadeh-T., H. Rezaei-S, M. Naghibi-S
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引用次数: 7

摘要

多智能体系统中的合作学习通常被期望能提高学习的质量和速度。当智能体能够识别他们之间的专家智能体并正确整合他们的知识时,这一点尤其正确。此外,当每个智能体中的强化学习信号能够在未知知识的搜索行为(探索)和已获得知识的学习行为(利用)之间取得平衡时,可以改善学习过程。提出了一种基于加权策略共享(WSS)的模糊动态合作学习方法,该方法在开发和探索行为之间取得了平衡。在加权策略共享方法中,智能体通过他们的专业程度来共享他们所学到的知识。将该策略应用于经典羊群问题时,当学习算法的参数由模糊例程动态确定时,学习质量和速度得到了进一步提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fuzzy adaptive algorithm for expertness based cooperative learning, application to herding problem
Cooperative learning in multi-agent systems is generally expected to improve both quality and speed of learning. This is particularly true when agents are able to recognize expert agents amongst themselves and integrate their knowledge properly. Additionally, the process of learning can be improved when the reinforcement learning signals in each agent can balance between searching behavior of the unknown knowledge (exploration) and learning behavior of the obtained knowledge (exploitation). In this paper, a fuzzy dynamic cooperative learning method, based on weighted strategy sharing (WSS), is introduced which draws a balance between exploitation and exploration behaviors. In the weighed strategy sharing method, agents share their learned knowledge by a measure of their expertness. The strategy, when applied to the classic herding problem, shows further improvement in quality and speed of learning when parameters of the learning algorithm are dynamically determined by a fuzzy routine.
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