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.

Package

Import the package relocor with

# pip install git+https://github.com/Bras-P/relocor.git

See notebook.ipynb for more details.

Citation

@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}
}