Using machine learning to inform harvest control rule design in complex fishery settings
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Abstract (arXiv)
In fishery science, harvest management of size-structured stochastic populations is a long-standing and difficult problem. Rectilinear precautionary policies based on biomass and harvesting reference points have now become a standard approach to this problem. While these standard feedback policies are adapted from analytical or dynamic programming solutions assuming relatively simple ecological dynamics, they are often applied to more complicated ecological settings in the real world. In this paper we explore the problem of designing harvest control rules for partially observed, age-structured, spasmodic fish populations using tools from reinforcement learning (RL) and Bayesian optimization. Our focus is on the case of Walleye fisheries in Alberta, Canada, whose highly variable recruitment dynamics have perplexed managers and ecologists. We optimized and evaluated policies using several complementary performance metrics. The main questions we addressed were: 1. How do standard policies based on reference points perform relative to numerically optimized policies? 2. Can an observation of mean fish weight, in addition to stock biomass, aid policy decisions?
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 53 references that are in the map. First posted on arXiv 16 Dec 2024.
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4 of 53 references.
References · 4
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2018A Tutorial on Bayesian Optimizationcited by 744
- 2021Deep Reinforcement Learning for Conservation Decisionscited by 5
- 2023Pretty darn good control: when are approximate solutions better than approximate modelscited by 1
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