Performance of a Markovian neural network versus dynamic programming on a fishing control problem - Archive ouverte HAL Access content directly
Journal Articles Probability, Uncertainty and Quantitative Risk Year : 2022

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

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Mathieu Laurière
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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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Dates and versions

hal-03891220 , version 1 (09-12-2022)

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Cite

Mathieu Laurière, Gilles Pagès, Olivier Pironneau. Performance of a Markovian neural network versus dynamic programming on a fishing control problem. Probability, Uncertainty and Quantitative Risk, In press, ⟨10.48550/arXiv.2109.06856⟩. ⟨hal-03891220⟩
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