mlsanityData to 14 Oct 2026

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

arXiv 2109.06856 ↗First posted 14 Sep 2021math.OC cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 2 of its 26 references that are in the map. First posted on arXiv 14 Sep 2021.

Builds on

2 of 26 references.

References · 2

  1. 2016Deep Learning Approximation for Stochastic Control Problemscited by 55
  2. 2018Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applicationscited by 16

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2022Langevin algorithms for Markovian Neural Networks and Deep Stochastic controlcited by 2
  2. 2023Policy Gradient Optimal Correlation Search for Variance Reduction in Monte Carlo simulation and Maximum Optimal Transportcited by 1