优化分布式形成控制使用标识符-关键-行为体强化学习用于一类随机非线性多代理系统
1Shandong University of Aeronautics, Binzhou, 256600, Shandong, China.
ISA transactions
|October 29, 2024
概括
本研究介绍了一种适应性强化学习 (RL) 方法,用于控制具有未知的动态的多代理系统 (MAS). 这种方法简化了对随机系统的最佳控制,从而实现了有效的分布式形成控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 多代理系统 (MAS) 经常面临未知的动态和随机环境的挑战.
- 为了实现最佳控制,传统的强化学习 (RL) 可能是复杂的,并且由于依赖汉密尔顿-雅各比-贝尔曼 (HJB) 方程,难以应用于随机系统.
研究的目的:
- 提出基于自适应强化学习 (RL) 的优化分布式训练控制策略.
- 为了应对在随机非线性单集成器多代理系统 (MAS) 中未知动态的挑战.
主要方法:
- 使用自适应识别神经网络 (NN) 来估计随机MAS的未知动态.
- 实施一种强化学习 (RL) 方法,利用演员和批评神经网络,以优化形成控制.
- 适应性RL定律是从一个正函数推导出来的,与基于HJB方程负梯度的方法相比,这简化了算法.
主要成果:
- 提出的方法成功地识别了未知的系统动态在预期下.
- 适应式RL框架使多代理系统的优化分布式形成控制成为可能.
- 理论证明和计算机模拟验证了优化控制方案的有效性.
结论:
- 开发的基于RL的自适应性形成控制对未知的随机非线性多代理系统有效.
- 简化的RL算法允许在随机动态系统中更顺利地实现.
- 该方法实现了分布式形成控制所需的控制目标.
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