在一个有界的2D网格世界中的多代理追逐逃避游戏中出现的行为
Sihan Xu1, Zhaohui Dang2,3
1School of Astronautics, Northwestern Polytechnical University, Xi'an, 710072, China.
Scientific reports
|August 11, 2025
概括
多代理强化学习使追逐逃避游戏中的复杂的自适应策略成为可能. 这项研究确定了新兴的合作行为和策略,在复杂的模拟中实现了高成功率.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 计算神经科学是一种神经科学.
背景情况:
- 多代理强化学习 (MARL) 对于在复杂环境中开发适应性策略至关重要.
- 追逐逃避游戏为研究新出现的行为和协调提供了一个强大的框架.
- 了解合作策略是推进自主系统的关键.
研究的目的:
- 在一个2D网格追逐逃跑游戏中,调查两个追逐者协调中出现的行为.
- 分析MARL算法在开发自适应追逐和逃避战术方面的有效性.
- 系统地识别和量化合作战略,使用K-means集群方法.
主要方法:
- 利用多代理强化学习 (MARL) 训练追求者和逃避者.
- 定义了六个基本的追求行动和21个用于双追求者协调的复合行动.
- 雇佣K-意味着对游戏轨迹进行集群,以检测新出现的行为模式和策略.
主要成果:
- 在1000个随机追逐-逃避试验中,追逐者的成功率达到了99.9%.
- 识别了诸如"惰的追求"和"蛇形的运动"之类的新兴行为.
- 量化了四个关键的合作追击策略:蛇形角圈,阶段角方法,同一侧边缘限制和刀侧翼攻击.
结论:
- 在追逐逃跑游戏中,MARL有效地产生复杂的,适应性的策略.
- K-means集群是一种可行的方法,用于识别和分析新出现的多代理行为.
- 该研究提供了关于行为出现和合作战略优化机制的重要见解.
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