使用投机对手模型进行决策
IEEE transactions on neural networks and learning systems
|April 10, 2024
概括
这项研究引入了一种新的AI技术用于多代理系统,该技术仅使用本地数据来模拟对手. 这种方法在复杂环境中增强决策能力,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 多代理系统 多代理系统
- 强化学习是一种强化学习.
背景情况:
- 对手建模在多代理系统中增强了代理决策.
- 当前的方法需要访问对手的观察和行动,这往往是不可行的.
- 需要使用仅使用局部信息的对手建模技术.
研究的目的:
- 介绍分布式对手辅助多代理演员-批评 (DOMAC),一种新的投机对手建模算法.
- 仅使用局部信息 (代理人的观察,行动,奖励) 实现有效的对手建模.
- 在复杂的多代理情景中改善代理决策和绩效.
主要方法:
- 在演员内部开发了投机的对手模型,以使用本地数据预测对手的行动.
- 纳入分布式批评模型来估计政策质量评估的回报分配.
- 正式推导出针对拟议的对手模型量身定制的政策梯度定理.
主要成果:
- 在多种多代理任务中,DOMAC成功地模拟了对手的行为.
- 在基准环境 (MPE,Pommerman,SMAC) 中与最先进的方法相比,表现优越.
- 与现有方法相比,在培训方面实现了更快的融合速度.
结论:
- DOMAC提供了一种有效的解决方案,用于仅使用局部信息进行对手建模.
- 该算法在具有挑战性的多代理设置中增强了代理性能和决策.
- DOMAC代表了人工智能投机对手建模的重大进步.
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