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Updated: May 9, 2025

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纳什-明马克斯策略多代理追逐-逃避游戏与强化学习
IEEE transactions on cybernetics
|May 2, 2025
概括
本研究介绍了多代理追击逃跑游戏的数据驱动最佳控制策略,提高了目标捕获. 这种新的方法使用强化学习和纳什-minmax策略来有效协调和对抗控制.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 游戏理论 游戏理论
- 人工智能的人工智能
背景情况:
- 多代理系统在追捕-逃避场景中存在复杂的协调挑战.
- 传统方法往往需要先前了解系统动态,限制了适应性.
- 在动态环境中实现强大的目标捕获仍然是一个重大问题.
研究的目的:
- 开发一个新的数据驱动的最佳控制政策,用于多代理追逐逃跑游戏.
- 设计一个框架,解决合作 (团队内部) 和对抗 (团队之间) 的互动.
- 确保可靠的目标捕获,而不需要事先了解代理动态.
主要方法:
- 作为一个两层结构,制定了追逐逃跑游戏:团队内部的非零和和团队之间的零和互动.
- 采用Nash-minmax策略来推导分布式纳什解决方案和对抗性的最坏情况政策.
- 利用政策之外的强化学习来实现数据驱动的最佳控制政策.
主要成果:
- 成功展示了一种数据驱动的方法来解决复杂的多代理追逐逃跑游戏.
- 拟议的纳什-最小值策略有效地处理了合作和对抗性代理行为.
- 通过独立于初始系统动态知识的控制政策实现了目标捕获.
结论:
- 拟议的数据驱动的最佳控制政策为多代理追踪逃避提供了一个强大而适应性的解决方案.
- 这个框架有效地将强化学习与游戏理论策略相结合,以提高表现.
- 该方法在复杂场景中实现目标捕获的实际适用性和有效性.
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