In this article, we present an intermittent framework for safe reinforcement learning (RL) algorithms. First, we develop a barrier function-based system transformation to impose state constraints while converting the original problem to an unconstrained optimization problem. Second, based on optimal derived policies, two types of intermittent feedback RL algorithms are presented, namely, a static and a dynamic one. We finally leverage an actor/critic structure to solve the problem online while guaranteeing optimality, stability, and safety. Simulation results show the efficacy of the proposed approach.

中文翻译：

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使用静态和动态事件生成器进行安全的间歇性强化学习。

在本文中，我们介绍了一种用于安全强化学习（RL）算法的间歇性框架。首先，我们开发基于势垒函数的系统转换以施加状态约束，同时将原始问题转换为无约束的优化问题。其次，基于最优派生策略，提出了两种间歇反馈RL算法，即静态和动态。我们最终利用演员/评论家的结构来在线解决问题，同时确保最佳性，稳定性和安全性。仿真结果表明了该方法的有效性。