在分布式多机器人系统中增强协调和安全的图形关闭关系推理:一个分散的强化学习方法
Tianshun Chang1, Yiping Ma1, Zhiqian Li1
1Merchant Shipping Academy, Shanghai Maritime University, Shanghai 201306, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
我们开发了一个图形门式变压器 (GGT),以改善多机器人的协调. 这种新的架构通过关注关键代理关系来提高复杂环境中的安全性和效率.
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:
- 在复杂的环境中协调多机器人系统是一项挑战.
- 当前的多代理强化学习 (MARL) 方法与动态的代理环境关系作斗争.
研究的目的:
- 引入图形门式变压器 (GGT) 来增强多机器人协调.
- 改进多代理系统中关于动态,因果关系的推理.
主要方法:
- 开发了图形门式变压器 (GGT) 架构.
- 动态构建一个战术关系图表明确的关系先验.
- 使用QMIX将GGT集成到一个具有分散执行的集中训练 (CTDE) 框架中.
- 使用一个封闭的注意力机制来集中变压器的推理.
主要成果:
- 基于GGT的系统实现了95.3%的覆盖效率,每集0.4次碰撞.
- 标准QMIX实现了60.3%的覆盖率,每集发生20.7次碰撞.
- 在动态障碍和传感器噪声的高保真模拟中取得了实质性改进.
结论:
- 通过使用动态关系图来明确限制注意力空间,可以增强多机器人的协调.
- 结构化,封闭的注意力机制是强大的集体自治的关键.
- GGT为安全和智能多机器人系统提供了有效的架构解决方案.
关键词:
无人驾驶地面车辆 (USV)避免碰撞,避免碰撞.语境推理 语境推理 语境推理合作控制合作控制.海上机器人技术的海洋机器人在海上安全,海上安全.多种代理强化学习的多种代理强化学习多模态感知多模态感知变压器变压器变压器变压器更多相关视频
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