Temporal Difference Learning for High-Dimensional PIDEs with Jumps

arXiv:2307.02766v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv construct cs.lg cs.na deep learning deep learning framework difference differential framework math.na networks neural networks paper process processes reinforcement reinforcement learning set temporal type