Performance of a Markovian neural network versus dynamic programming on a fishing control problem
Optimization and Control
2021-09-15 v1 Machine Learning
Abstract
Fishing quotas are unpleasant but efficient to control the productivity of a fishing site. A popular model has a stochastic differential equation for the biomass on which a stochastic dynamic programming or a Hamilton-Jacobi-Bellman algorithm can be used to find the stochastic control -- the fishing quota. We compare the solutions obtained by dynamic programming against those obtained with a neural network which preserves the Markov property of the solution. The method is extended to a similar multi species model to check its robustness in high dimension.
Cite
@article{arxiv.2109.06856,
title = {Performance of a Markovian neural network versus dynamic programming on a fishing control problem},
author = {Mathieu Laurière and Gilles Pagès and Olivier Pironneau},
journal= {arXiv preprint arXiv:2109.06856},
year = {2021}
}