整体强化基于学习的动态事件触发的USVs的非零和游戏
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究介绍了在动态事件触发游戏中用于无人地面车辆 (USV) 的综合强化学习方法,在约束条件下实现纳什平衡. 该方法通过使用事件触发控制和神经网络来节约资源.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 游戏理论 游戏理论
- 人工智能的人工智能
背景情况:
- 无人驾驶地面车辆 (USV) 需要强大的控制策略来导航和执行任务.
- 在非零和 (NZS) 游戏中实现纳什平衡对于多代理协调至关重要.
- 国家和输入约束在USV控制系统中构成重大挑战.
研究的目的:
- 为动态事件触发的NZS游戏开发一个综合强化学习 (IRL) 方法.
- 使USV能够在遵守状态和输入约束的同时实现纳什平衡.
- 通过事件触发控制来提高计算效率和降低网络负载.
主要方法:
- 设计了一个映射功能,以确保USV状态和控制保持在安全的操作环境中.
- 基于IRL的合汉密尔顿 - 雅各比方程得出,绕过了对显式系统动态的需求.
- 开发了一个动态事件触发的控制机制,改进了静态方法.
- 关键神经网络被用来接近每个代理的价值函数和控制策略.
主要成果:
- 拟议的IRL方法成功地实现了在约束条件下USVs的纳什平衡.
- 动态事件触发方法节省了计算资源和网络带宽.
- 严格的证明证实了系统状态和重量估计错误的统一的最终边界性.
- 模拟实验验证了开发的控制策略的有效性.
结论:
- 综合强化学习方法为USV系统中受约束的NZS游戏提供了有效的解决方案.
- 事件触发控制在合作USV操作中显著优化资源利用.
- 该方法通过模拟证明了强大的理论保证和实际适用性.
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