Coordinating Multiple Agents via Reinforcement Learning
Coordinating Multiple Agents via Reinforcement Learning
复制标题
通过强化学习协调多个智能体
DOI：
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发表时间：
2005
影响因子：
1.9
通讯作者：
K. M. Goh
中科院分区：
文献类型：
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作者：
Gang Chen;Zhonghua Yang;Hao He;K. M. Goh
在本文中，我们试图使用强化学习技术来解决面向任务的环境中的代理协调问题。提出了模糊主观任务结构模型（FSTS），用于描述一般Agent的协调。我们表明，代理协调问题建模FSTS是一个决策理论规划（DTP）问题，强化学习可以应用。两种学习算法，“粗粒度”和“细粒度”，提出了解决代理协调行为在两个不同的水平。“粗粒度”算法在一个级别上运行，处理硬系统约束，而“细粒度”算法在另一个级别上运行，处理软约束。我们认为，重要的是要明确建模和探索特定的坐标（特别是系统约束）的信息，这两个算法的基础和属性的算法的有效性。算法的收敛性和实验证明是有效的。
In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, ‘‘coarse-grained’’ and ‘‘fine-grained’’, are proposed to address agents coordination behavior at two different levels. The ‘‘coarse-grained’’ algorithm operates at one level and tackle hard system constraints, and the ‘‘fine-grained’’ at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.