一个基于局部信息聚合的多代理强化学习,用于机器人队伍动态任务分配
IEEE transactions on neural networks and learning systems
|April 23, 2025
概括
这项研究介绍了机器人群的新算法,以优化在不断变化的环境中任务分配. 当地信息聚合-多代理深度决定性政策梯度 (LIA-MADDPG) 算法增强了机器人的合作和适应性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 多代理系统 多代理系统
背景情况:
- 在动态环境中优化任务分配对于机器人群的效率至关重要.
- 现有的方法往往缺乏分散网络的灵活性和可扩展性.
研究的目的:
- 开发一种新的框架,用于在机器人群中进行强大且可扩展的任务分配.
- 增强机器人在动态,部分可观测环境中的合作和适应能力.
主要方法:
- 引入了一个分散的部分可观察的马尔科夫决策过程 (Dec-POMDP) 框架.
- 开发了局部信息聚合-多代理深度决定性政策梯度 (LIA-MADDPG) 算法.
- 集成了一个本地信息聚合 (LIA) 模块用于集中培训,以及用于分布式执行的战略改进方法.
主要成果:
- 当与现有的集中培训和分散执行 (CTDE) 方法集成时,LIA模块显著提高了性能.
- 与传统算法相比,LIA-MADDPG表现出卓越的可扩展性,更快的适应性,并保持了稳定性和融合速度.
- 经验评估验证了算法在动态任务分配中的有效性.
结论:
- LIA-MADDPG在机器人群的动态任务分配方面取得了重大进展.
- 该框架增强了当地协作和适应性战略执行,以提高群体表现.
- 这种方法有可能用于需要灵活机器人合作的现实应用.
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