对于受约束的马尔科夫决策过程,更快的算法和更清晰的分析
Tianjiao Li1, Ziwei Guan2, Shaofeng Zou3
1Georgia Institute of Technology, United States of America.
Operations research letters
|December 15, 2025
概括
本研究引入了对受约束马尔科夫决策过程 (CMDPs) 的高效初级-双元方法. 这种新的方法加速了全球最佳的趋同,大大改善了现有的CMDP优化方法.
科学领域:
- 人工智能的人工智能
- 运营研究 运营研究
- 机器学习 机器学习
背景情况:
- 受到约束的马尔科夫决策过程 (CMDPs) 涉及代理商在实用性/成本约束下最大化奖励.
- 现有的CMDPs的原始-双元方法在融合效率方面面临挑战.
研究的目的:
- 为解决CMDPs开发一种新且高效的初级-双元方法.
- 改进融合复杂性,以在CMDP中找到全球最佳值.
主要方法:
- 整合调整与内斯特罗夫加速梯度方法.
- 一个为CMDPs量身定制的新的初级-双元优化框架.
主要成果:
- 拟议的方法实现了对全球最佳的趋同,其复杂性为O~{1/ε}.
- 这与现有的初级-双元方法相比是一个显著的改进,复杂度因子的改进为O{\displaystyle O}1/ε{\displaystyle O}/ε{\displaystyle O}1/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε{\displaystyle O}/ε}).
结论:
- 新的初级-双元方法为CMDP提供了更有效的解决方案.
- 这种进步对强化学习和在约束下做决定有影响.
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