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游戏中的多代理强化学习:研究和应用
Haiyang Li1, Ping Yang1, Weidong Liu1
1High-Tech Institute of Xi'an, Xi'an 710038, China.
Biomimetics (Basel, Switzerland)
|June 25, 2025
概括
这项研究结合了多代理强化学习 (MARL) 和游戏理论,灵感来自生物系统. 它增强了集体智能,以便在智能城市等动态环境中进行复杂的决策.
科学领域:
- 人工智能的人工智能
- 计算游戏理论 计算游戏理论
- 生物启发的计算 生物启发的计算
背景情况:
- 生物系统表现出自我组织的智能.
- 在复杂的系统中,将游戏理论理性和多代理适应性联系起来至关重要.
- 现有的框架缺乏将生物灵感原则与集体决策的先进人工智能相结合.
研究的目的:
- 系统地审查多代理强化学习 (MARL) 和游戏理论的融合.
- 阐明这种集成范式在动态开放环境中集体智能决策的潜力.
- 确定技术突破和绘制开发路径,以加强多代理系统.
主要方法:
- 利用随机游戏和广泛形式的游戏理论框架.
- 建立基于价值函数优化,政策梯度学习和在线搜索规划的方法分类.
- 结合生物灵感优化方法,包括进化计算和基于人口的学习.
主要成果:
- 开发了一种方法学分类,阐明了MARL和游戏理论中的算法进步.
- 识别了用于智慧城市场景的MARL应用中的技术突破 (例如,智能交通,无人机调度).
- 强调了生物灵感机制对动态战略生成和勘探效率的有效性.
结论:
- 马尔和游戏理论的整合,通过生物启发的计算来增强,为集体智能提供了巨大的潜力.
- 这种跨学科的方法为开发能够在复杂,动态环境中进行最佳决策的先进多代理系统提供了路线图.
- 调查结果揭示了集团决策的核心原则,并绘制了技术发展路径.
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