多代理诱导政策优化 多代理诱导政策优化
IEEE transactions on neural networks and learning systems
|September 9, 2025
概括
本研究引入了一种新的多代理诱导政策优化 (MAIPO) 方法,用于复杂的强化学习任务. MAIPO确保代理人学习改进政策,并鼓励勘探以避免局部最佳.
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:
- 由于协调多个代理的复杂性,多代理强化学习 (RL) 带来了重大挑战.
- 现有的政策优化方法与高维的状态动作空间和代理之间的依赖性作斗争.
研究的目的:
- 为多代理增强学习环境开发一种新的政策优化框架.
- 确保单调的政策改进,增强合作伙伴的勘探能力.
主要方法:
- 推导出一个一般的信任区域,考虑多个代理机构设置中的子政策组合.
- 提出了一个诱导性目标函数,包含一个政策距离成本.
- 实施和评估了多代理诱导政策优化 (MAIPO) 方法.
主要成果:
- MAIPO展示了对代理人的单调改进政策.
- 政策的远程成本有效地鼓励了勘探,并防止过早地趋同到当地最佳.
- 对风电场控制和基准任务的模拟结果显示,与现有方法相比,性能优越.
结论:
- 拟议的MAIPO方法为复杂的多剂增强学习问题提供了可靠的解决方案.
- MAIPO平衡了信任地区的政策稳定性和更好的绩效的探索.
- 这种方法在现实应用中是有效的,例如风电场控制.
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