基于强化学习的闭环控制
Ruimin Dan1, Honghui Zhang1, Jianchao Bai1
1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, P. R. China.
International journal of neural systems
|October 17, 2025
概括
本研究介绍了一种适应性深度强化学习策略,用于治疗,使用深度决定性政策梯度 (DDPG) 和模型不可知的元学习 (MAML). 这种新的方法显著减少了发作的频率和持续时间,同时优化了能源效率.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 治疗仍然具有挑战性,通常依赖于具有有限适应能力的开放循环系统.
- 目前的深度大脑刺激 (DBS) 策略缺乏个性化的实时调整,以实现最佳的控制.
研究的目的:
- 开发一种新的适应性深度大脑刺激 (DBS) 控制策略,用于使用深度强化学习治疗.
- 为了增强发作减少,最大限度地减少能源消耗,并在各种患者场景中实现个性化治疗.
主要方法:
- 建立了皮质 - thalamus循环的随机干扰模型,将神经调制问题转化为马尔科夫决策过程.
- 深度决定性政策梯度 (DDPG) 算法用于适应性动态调节刺激参数.
- 模型无意识的元学习 (MAML) 与DDPG集成,以创建具有转移学习能力的协作控制策略.
主要成果:
- 适应性DBS控制策略在各种模拟场景中显著降低了发作的频率和持续时间.
- 与开放循环系统相比,闭环系统的能量损失减少了[公式:参阅文本],非状态增加了[公式:参阅文本].
- 在不同患者情景中,MAML-DDPG策略显示出显著的优势,表明强大的转移学习能力.
结论:
- 基于深度强化学习的拟议适应性DBS控制策略为治疗提供了一种有效的方法.
- 整合MAML提高了战略的适应性和个性化,为精确的管理提供了至关重要的技术支持.
- 这项研究为更智能,更高效的闭环神经调节系统铺平了道路.
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