论文标题
连续时间随机最佳停止问题和增强学习算法
Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm
论文作者
论文摘要
在本文中，我们研究了所谓的探索框架中的最佳停止问题，其中代理在当前状态上随机调节操作，熵登记的项被添加到奖励功能中。这样的转换将最佳停止问题减少到标准的最佳控制问题。我们得出相关的HJB方程，并证明其解决性。此外，我们给出了政策迭代的收敛速度，并与经典的最佳停止问题进行了比较。基于理论分析，设计了增强学习算法，并证明了几种模型的数值结果。
In this paper, we study the optimal stopping problem in the so-called exploratory framework, in which the agent takes actions randomly conditioning on current state and an entropy-regularized term is added to the reward functional. Such a transformation reduces the optimal stopping problem to a standard optimal control problem. We derive the related HJB equation and prove its solvability. Furthermore, we give a convergence rate of policy iteration and the comparison to classical optimal stopping problem. Based on the theoretical analysis, a reinforcement learning algorithm is designed and numerical results are demonstrated for several models.
