Optimal Correlation search using Reinforcement Learning for variance reduction in Monte Carlo simulation
Implementation of the package relocor: REinforcement Learning Optimal CORrelation search, a stochastic control and reinforcement-learning based method for variance reduction in Monte Carlo simulation of stochastic differential equations.
See the arXiv pre-publication for more details.
Import the package relocor with
# pip install git+https://github.com/Bras-P/relocor.git
See notebook.ipynb for more details.
@ARTICLE{2023arXiv230712703B,
author = {{Bras}, Pierre and {Pag{\`e}s}, Gilles},
title = "{Policy Gradient Optimal Correlation Search for Variance Reduction in Monte Carlo simulation and Maximum Optimal Transport}",
journal = {arXiv e-prints},
keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Optimization and Control},
year = 2023,
month = jul,
eid = {arXiv:2307.12703},
pages = {arXiv:2307.12703},
archivePrefix = {arXiv},
eprint = {2307.12703},
primaryClass = {stat.ML},
adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230712703B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}