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接近实时的在线强化学习与同步或异步更新
Mircea-Bogdan Radac1, Darius-Pavel Chirla2
1Department of Automation and Applied Informatics, Politehnica University of Timisoara, Bvd. V. Parvan, 2, 300223, Timisoara, Romania. mircea.radac@upt.ro.
Scientific reports
|May 17, 2025
概括
本研究介绍了复杂系统控制的在线强化学习 (RL) 方法,通过交叉系统交互和神经网络训练实现近实时学习. 该方法通过模拟和硬件实验得到验证,显示出实际应用的前景.
科学领域:
- 控制系统工程 控制系统工程
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 强化学习 (RL) 对于控制复杂的系统是有效的,但在实际应用中面临着局限性,因为在交叉学习和交互方面存在挑战.
- 将神经网络的复杂性与实时学习能力相结合,仍然是控制系统中广泛采用RL的一个重大障碍.
研究的目的:
- 提出并验证在线强化学习 (RL) 解决方案,解决交叉环境交互和学习步骤的局限性.
- 为使用RL的复杂和未知的动态系统提供近实时学习能力.
主要方法:
- 开发了一个在线学习解决方案,使用过去的信号和参考模型/输入状态编码系统状态.
- 价值函数和控制器神经网络通过反向传播使用系统交互数据进行在线训练.
- 该方法在模拟和实验硬件设置中使用模型-参考跟踪控制问题进行测试.
主要成果:
- 通过模拟和实验案例研究,拟议的在线RL方法被证明是有效的.
- 使用两个高级软件包,将同步和异步更新之间的性能运行时间进行了比较.
- 软件挑战和代码运行时数的分析为实际实施提供了洞察力.
结论:
- 在线同步RL显示出低级系统具有强大的潜力,具有快速动态,与实时要求保持一致.
- 异步在线RL使得扩展到更高维度系统和更快的动态更容易,即使在非硬实时场景中.
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