对竞争的多代理动态系统的培训策略
Haotian Dai1, Marco G Mazza2, Yunyun Li3
1Loughborough University, Department of Physics, Loughborough LE11 3TU, United Kingdom.
Physical review. E
|January 21, 2026
概括
在多代理系统中的强化学习表明,在使用混合策略时,对捕食者-猎物动态的顺序训练是优越的. 只有在自然政策下,同时进行培训才更好.
科学领域:
- 多代理系统是多代理系统.
- 活动物质物理学 活动物质物理学
- 计算智能是一种计算智能.
背景情况:
- 在多代理系统中,竞争动态至关重要.
- 活性物质系统表现出复杂的新兴行为.
- 强化学习 (RL) 为复杂环境中的培训人员提供了一个强大的框架.
研究的目的:
- 调查同步与顺序训练协议对掠食者-猎物活性物质系统中药剂性能的影响.
- 为了比较自然和混合政策在多代理系统的强化学习的有效性.
- 确定竞争性多剂活性物质模拟的最佳培训策略.
主要方法:
- 利用强化学习训练两个活跃的布朗粒子 (捕食者) 捕获十个被动的布朗粒子 (猎物).
- 实施并比较了两个培训协议:同时和顺序.
- 评估了两个不同的政策:自然政策 (固定时间更新) 和混合政策 (最佳绩效参数).
主要成果:
- 代理商的表现各不相同,其中一个代理商通常表现优于其他代理商.
- 在自然政策下,同步培训产生了更好的结果.
- 当采用混合政策时,顺序培训被证明是更有效的策略.
结论:
- 在同步和连续培训协议之间做出选择取决于所采用的政策.
- 混合政策显著提高了对具有竞争力的多剂活性物质系统的连续培训的有效性.
- 采用混合政策的连续培训为在复杂的竞争场景中优化代理业绩提供了更强大的方法.
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