A survey on multi-agent reinforcement learning and its application

Zepeng Ning, Lihua Xie
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Abstract

Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper presents a comprehensive survey of MARL and its applications. We trace the historical evolution of MARL, highlight its progress, and discuss related survey works. Then, we review the existing works addressing inherent challenges and those focusing on diverse applications. Some representative stochastic games, MARL means, spatial forms of MARL, and task classification are revisited. We then conduct an in-depth exploration of a variety of challenges encountered in MARL applications. We also address critical operational aspects, such as hyperparameter tuning and computational complexity, which are pivotal in practical implementations of MARL. Afterward, we make a thorough overview of the applications of MARL to intelligent machines and devices, chemical engineering, biotechnology, healthcare, and societal issues, which highlights the extensive potential and relevance of MARL within both current and future technological contexts. Our survey also encompasses a detailed examination of benchmark environments used in MARL research, which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios. In the end, we give our prospect for MARL and discuss their related techniques and potential future applications.

多代理强化学习及其应用调查
多代理强化学习(MARL)是一个发展迅速的领域。本文全面介绍了 MARL 及其应用。我们追溯了 MARL 的历史演变,重点介绍了其进展情况,并讨论了相关的研究工作。然后,我们回顾了解决固有挑战的现有工作以及那些专注于各种应用的工作。我们将重新审视一些具有代表性的随机博弈、MARL 手段、MARL 的空间形式和任务分类。然后,我们深入探讨了 MARL 应用中遇到的各种挑战。我们还讨论了超参数调整和计算复杂性等关键操作问题,这些问题在 MARL 的实际应用中至关重要。随后,我们对 MARL 在智能机器和设备、化学工程、生物技术、医疗保健和社会问题中的应用进行了全面概述,突出了 MARL 在当前和未来技术背景下的广泛潜力和相关性。我们的调查还包括对 MARL 研究中使用的基准环境的详细审查,这些基准环境有助于评估 MARL 算法,并证明 MARL 对各种应用场景的适应性。最后,我们展望了 MARL 的前景,并讨论了其相关技术和未来的潜在应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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