As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational quantum circuits (VQCs) have been noticed for their ability to carry out quantum deep reinforcement learning (QRL). This article verifies the potential of QRL, which will be further realized by implementing quantum multiagent reinforcement learning (QMARL) from QRL, especially for Internet-connected autonomous multirobot control and coordination in smart factory applications. However, the extension is not straightforward due to the nonstationarity of classical MARL. To cope with this, the centralized training and decentralized execution (CTDE) QMARL framework is proposed under the Internet connection. A smart factory environment with the Internet of Things (IoT)-based multiple agents is used to show the efficacy of the proposed algorithm. The simulation corroborates that the proposed QMARL-based autonomous multirobot control and coordination performs better than the other frameworks.

中文翻译：

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用于智能工厂管理中联网多机器人协调的量子多智能体神经网络


作为学术界和工业界感兴趣的最新领域之一，量子计算已经引起了极大的关注。在量子计算的各个主题中，变分量子电路（VQC）因其执行量子深度强化学习（QRL）的能力而受到关注。本文验证了 QRL 的潜力，通过实施 QRL 的量子多智能体强化学习（QMARL）将进一步实现 QRL 的潜力，特别是对于智能工厂应用中的互联网连接的自主多机器人控制和协调。然而，由于经典 MARL 的非平稳性，推广并不简单。为了解决这个问题，在互联网连接下提出了集中训练和分散执行（CTDE）QMARL框架。使用具有基于物联网（IoT）的多个代理的智能工厂环境来展示所提出算法的功效。仿真证实所提出的基于 QMARL 的自主多机器人控制和协调性能优于其他框架。