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Preprints, Working Papers, ... Year : 2022

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

Mathieu Laurière
  • Function : Author
Olivier Pironneau
  • Function : Author

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.

Dates and versions

hal-03890777 , version 1 (08-12-2022)

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Mathieu Laurière, Gilles Pagès, Olivier Pironneau. Performance of a Markovian neural network versus dynamic programming on a fishing control problem. 2022. ⟨hal-03890777⟩
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