资源的最佳协调:一种来自强化学习的解决方案
Guozhong Zheng1, Weiran Cai2, Guanxiao Qi3
1Shaanxi Normal University, School of Physics and Information Technology, Xi'an 710062, People's Republic of China.
Physical review. E
|January 21, 2026
概括
当个人平衡从经验中学习与探索新策略时,就能实现最佳的资源配置. 打破这种平衡会导致协调不良,甚至比随机行为更糟糕.
科学领域:
- 计算经济学是计算机经济学.
- 游戏理论的游戏理论.
- 行为经济学是一种行为经济学.
背景情况:
- 在面临竞争的自然和社会系统中,有效的资源配置至关重要.
- 少数人游戏 (MG) 模拟资源竞争,但先行解决方案缺乏适应性策略.
研究的目的:
- 将强化学习应用于少数群体游戏,捕捉自适应策略演变.
- 调查如何平衡勘探和开采影响集体行为和资源分配.
主要方法:
- 利用强化学习框架在MG中进行适应性决策.
- 分析了勘探开发平衡对协调和资源利用的影响.
- 研究了最佳协调及其破坏稳定的潜在机制.
主要成果:
- 最佳的资源配置发生在个人平衡基于经验的开发与随机探索时.
- 偏离这种平衡会导致部分协调甚至反协调.
- 在行动偏好中打破对称性是最佳协调和最大限度的资源利用的基础.
结论:
- 强化学习为少数人游戏提供了一种新的方法,考虑了适应性策略.
- 这些发现提供了通过平衡的个人决策来实现高效的资源分配的见解.
- 该研究强调了探索在防止低于最佳的集体结果方面的重要性.
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