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Temporal Difference Learning for High-Dimensional PIDEs with Jumps by Lu, Liwei; Guo, Hailong; Yang, Xu; Zhu, Yi is a scholarly article available to read on EtoBox.

What is Temporal Difference Learning for High-Dimensional PIDEs with Jumps about?

In this paper, we propose a deep learning framework for solving high-dimensional partial integro-differential equations (PIDEs) based on the temporal difference learning. We introduce a set of Levy processes and construct a corresponding reinforcement learning model. To simulate the entire process, we use deep neural networks to represent the solutions and non-local terms of the equations. Subsequently, we train the networks using the temporal difference error, termination condition, and properties of the non-local terms as the loss function. The relative error of the method reaches O(10^{-3}) in 100-dimensional experiments and O(10^{-4}) in one-dimensional pure jump problems. Additionally, our method demonstrates the advantages of low computational cost and robustness, making it well-suited for addressing problems with different forms and intensities of jumps.

Author

Lu, Liwei; Guo, Hailong; Yang, Xu; Zhu, Yi

Published

2023

Language

EN