We study challenges using reinforcement learning in controlling energy systems, where apart from performance requirements, one has additional safety requirements such as avoiding blackouts. We detail how these safety requirements in real-time temporal logic can be strengthened via discretization into linear temporal logic (LTL), such that the satisfaction of the LTL formulae implies the satisfaction of the original safety requirements. The discretization enables advanced engineering methods such as synthesizing shields for safe reinforcement learning as well as formal verification, where for statistical model checking, the probabilistic guarantee acquired by LTL model checking forms a lower bound for the satisfaction of the original real-time safety requirements.

探讨强化学习在控制能源系统中的挑战，其中除了性能要求外，还有额外的安全要求，如避免停电。我们详细介绍了如何通过离散化成线性时间逻辑（LTL）来加强实时时间逻辑中的这些安全要求，以使满足LTL公式意味着满足原始的安全要求。离散化使得先进的工程方法成为可能，如为安全强化学习合成防护屏障以及形式验证，其中对于统计模型检查，由LTL模型检查获得的概率保证形成原始实时安全需求满足的下限。

基于采样规范的智能能源系统学习控制的保护