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arXiv · 2109.06856

Performance of a Markovian neural network versus dynamic programming on a fishing control problem

Mathieu Laurière·Gilles Pagès·Olivier Pironneau
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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.

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Mathieu Laurière, Gilles Pagès, Olivier Pironneau. 2021-09-14. Performance of a Markovian neural network versus dynamic programming on a fishing control problem. https://arxiv.org/abs/2109.06856

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