Wednesday, April 24, 2024 - 16:15 in V3-201
Reinforcement learning for optimal stopping problems with exploration: A singular control formulation
A talk in the Bielefeld Stochastic Afternoon series by
Jodi Dianetti from Bielefeld University
| Abstract: |
We address continuous time and state-space optimal stopping problems from the reinforcement learning point of view. We first introduce a formulation of the stopping problem via singular controls, which allows the agent to randomize strategies. Then, we consider a regularized version of the problem by penalizing the cumulative residual entropy of the chosen strategy to incentivize exploration. The regularized version of the problem is then studied using the dynamic programming principle approach, which allows us to characterize the unique optimal exploratory strategy. In a benchmark example, we are able to solve the regularized problem explicitly, thus allowing us to study the effect of the entropy regularization and the vanishing entropy limit.
The talk is based on a joint work together with Giorgio Ferrari and Renyuan Xu.
Within the CRC this talk is associated to the project(s): B8 |
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