[Submitted on 17 Sep 2026]
Title:Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies
View a PDF of the paper titled Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies, by Debartha Paul and 1 other authors
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Abstract:Quasars are luminous objects in the universe that exhibit stochastic brightness variations encoding information about the supermassive black holes powering them, and modeling these variations from ground-based survey data time series, known as light curves, is a statistical challenge. This paper reviews how stochastic differential equations (SDEs) have been adapted with neural network parameterizations to overcome this challenge in history. We create the Continuous-Delayed-Memory Stochastic Gradient Descent which depend on the past state of the discrete iteration process. We performed the simulation on some 2-dimensional landscape and observed some wider-exploration and more precise convergent behavior compared to Vanilla SGD by adjusting hyperparameters. Besides, we proposed a reinforcement learning structure with continuous time policy gradients for exploratory policies without solving HJB PDE, and we show that its optimality conditions recover the Gibbs policy of previous works.
Comments: Keywords: Stochastic process, Stochastic gradient descent, Continuous-Delayed-Memory Stochastic Gradient Descent, Stochastic Delay Differential Equation, Reinforcement Learning, Adjoint method
Subjects:
Machine Learning (cs.LG); Applications (stat.AP)
Cite as: arXiv:2609.20906 [cs.LG]
(or arXiv:2609.20906v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.20906
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Juncheng Yi [view email] [v1] Thu, 17 Sep 2026 15:38:37 UTC (4,976 KB)
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