操作优化 电解过程的决策 使用离线增强学习
IEEE transactions on cybernetics
|November 25, 2025
概括
本研究引入了一种新的方法,用于在使用线下强化学习的电解中优化安全操作. 该方法确保政策是可行的,并超越现有的政策,同时满足严格的工业安全标准.
科学领域:
- 工业过程优化 工业过程优化
- 人工智能的人工智能
- 化学工程是化学工程的重要组成部分.
背景情况:
- 电解产生复杂的数据集,具有多样化和风险的政策.
- 离线强化学习在确保工业操作的安全性和可行性方面面临挑战.
- 现有的方法在多类行为政策中与分布转移作斗争.
研究的目的:
- 为优化电解操作,开发一种线下多目标强化学习方法,具有多类政策约束.
- 为了使优化政策的学习超过行为政策,同时遵守安全要求.
- 在多类行为政策中解决分布转移问题.
主要方法:
- 提出了一个线下多目标强化学习框架,具有多类型的政策约束.
- 使用混合高斯变量自编码器 (GMVAE) 进行行为克隆和政策约束.
- 设计了一个演员-批评架构,对操作性能和安全进行了独立的批评.
- 引入了一个安全批评网络,以强制执行工业安全要求.
主要成果:
- 提出的方法学习了一个优化策略,优于行为策略.
- 学习的政策成功地满足了严格的工业安全要求.
- 在真实世界电解数据上的实验结果显示,与其他线下强化学习算法相比,性能优越.
- 有效地缓解了多类型行为政策上的分配转移问题.
结论:
- 开发的线下多目标增强学习方法是有效的安全操作优化在电解.
- 该方法为具有安全限制的复杂工业场景提供了可靠的解决方案.
- 该方法在这个领域显示了与现有的线下强化学习算法相比的显著优势.
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