Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm
Optimization and Control
2023-09-04 v3 Mathematical Finance
Abstract
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.
Cite
@article{arxiv.2208.02409,
title = {Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm},
author = {Yuchao Dong},
journal= {arXiv preprint arXiv:2208.02409},
year = {2023}
}