Abstract Multi-agent systems can limit the control problem in complex production systems and solve them more efficiently. However, they often show local optimization tendencies. This paper presents a novel approach for a cooperative multi-agent system, which uses reinforcement learning and considers global key performance indicators. For this purpose, a central deep q-learning module transfers its knowledge to the cooperative order agents. The order agent's experience is stored in a replay memory for subsequent reinforcement learning. Interdependencies between the characteristics of nonlinear production systems and learning parameters are investigated and the performance is evaluated in comparison to conventional methods of production control.

中文翻译：

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使用强化学习进行生产控制的协同多智能体系统

摘要 多智能体系统可以限制复杂生产系统中的控制问题并更有效地解决这些问题。然而，它们经常表现出局部优化趋势。本文提出了一种协作多智能体系统的新方法，该方法使用强化学习并考虑全局关键性能指标。为此，中央深度 q 学习模块将其知识转移到合作订单代理。订单代理的经验存储在重放记忆中，用于后续的强化学习。研究了非线性生产系统的特性和学习参数之间的相互依赖性，并与传统的生产控制方法进行了比较，评估了性能。