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默契机制:将个人性预训练与多个代理商的对抗性协调联系起来

Shiqing Yao¹, Jiajun Chai², Haixin Yu¹

¹Tsinghua Shenzhen International Graduate School, Tsinghua University, Nanshan District, Shenzhen, 518071, China.

Neural networks : the official journal of the International Neural Network Society

|October 9, 2025

概括

本研究介绍了预训练的默示行为协调 (PTBC),这是一个新的框架,增强了多代理强化学习 (MARL). 通过整合空间关系,PTBC通过完善代理协调,提高对抗任务的性能.

科学领域:

人工智能的人工智能
机器学习机器学习
机器人技术机器人技术机器人技术

背景情况:

当前的多代理强化学习 (MARL) 算法经常忽视空间关系,限制了对抗场景中的协调效率.
有效的多代理对抗协调对于复杂的任务至关重要,但仍然是一个重大挑战.

研究的目的:

引入一个新的框架,即预训练的默示行为协调 (PTBC),以提高MARL在对抗性协调中的效率.
通过利用空间关系及其趋势来改进代理政策更新.

主要方法:

一个两阶段的框架,包括一个默认的预训练阶段和一个集中的对抗性训练阶段.
开发一个模式机制和一个默契机制,以整合和利用代理人之间的空间关系.
利用预先训练的网络来加强在对抗训练期间的空间定位和协调.

主要成果:

与现有的算法相比,PTBC在掠食者猎物和StarCraft多代理挑战 (SMAC) 环境中表现出卓越的性能.
可视化证实,经过预先训练的默契行为的代理人通过结合代理人之间的关系来实现更有利的协调.
废弃性研究证实了默许指导的关键作用和PTBC框架的一般适用性.

结论:

拟议的PTBC框架通过默认的行为预训练来整合空间关系,有效地增强了多代理对抗协调.
该方法提供了更有效的学习过程,并改善了复杂的多代理系统中的协调.
PTBC提供了一种可通用的方法,用于改善MARL在对抗性环境中的性能.

关键词:

多个代理人的对抗协调协调.强化学习是一种强化学习.默契的预训练,就是默契的预训练.

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