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

arXiv: https://arxiv.org/abs/2109.06856 · HTML: https://mlsanity.com/paper/2109.06856/

## 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 (most cited first, 2 of 2)

- [Deep Learning Approximation for Stochastic Control Problems](https://mlsanity.com/paper/1611.07422/) (2016)
- [Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications](https://mlsanity.com/paper/1812.05916/) (2018)

## Built on by (most cited first, 2 of 2)

- [Langevin algorithms for Markovian Neural Networks and Deep Stochastic control](https://mlsanity.com/paper/2212.12018/) (2022)
- [Policy Gradient Optimal Correlation Search for Variance Reduction in Monte Carlo simulation and Maximum Optimal Transport](https://mlsanity.com/paper/2307.12703/) (2023)

## 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.