Performance of a Markovian neural network versus dynamic programming on a fishing control problem
This work addresses fishing control optimization for resource managers, but it is incremental as it applies a neural network variant to an existing model.
The paper tackled the problem of determining optimal fishing quotas by comparing solutions from dynamic programming with those from a Markovian neural network, finding that the neural network approach performed competitively and was robust in high-dimensional multi-species scenarios.
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.