基于深度Q网络的概率有限态机器的反稳定
Hui Tian1, Xin Su1, Yanfang Hou2
1Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China.
Frontiers in computational neuroscience
|May 17, 2024
概括
本研究引入了深度Q网络 (DQN) 来稳定概率有限状态机器 (PFSM),克服了复杂系统传统方法的局限性. 这种新的方法有效地计算状态反稳定器,以进行增强的系统分析.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 有限状态机器 (FSM) 是制造业和医疗保健中的关键数学模型.
- 传统的FSM分析和设计方法面临着方便性和计算复杂性的挑战.
- 概率有限态机 (PFSM) 存在独特的稳定性挑战.
研究的目的:
- 解决分析和设计有限状态机器的传统方法的局限性.
- 开发一种有效的算法来稳定概率有限状态机器.
- 利用深度学习技术来解决FSM中复杂的控制问题.
主要方法:
- 回忆起关于马尔科夫决策过程,epsilon贪策略和深度Q网络 (DQN) 的初步研究.
- 导出了PFSMs.的必要和足够的稳定性条件.
- 将PFSM反稳定问题转化为可用DQN解决的优化问题.
主要成果:
- 开发了一种使用DQN的新算法,用于计算PFSMs的状态反稳定器.
- 证明DQN克服了传统Q学习的有限容量问题.
- 提出的方法有效地处理高维和复杂的系统.
结论:
- 基于DQN的方法为PFSM稳定提供了高效和可扩展的解决方案.
- 这种方法提高了基于FSM的复杂系统的分析和设计能力.
- 这些发现为控制理论中应用深度强化学习开辟了新的途径.
相关概念视频
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