用强化学习优化复杂渔业的捕捞管理规则，提升决策精度。
Using machine learning to inform harvest control rule design in complex fishery settings
- 结合强化学习与贝叶斯优化设计动态捕捞策略
- 相比传统参考点政策，新策略在波动环境中表现更优
- 引入平均鱼重信息可进一步提升管理效果，适合复杂渔业
在渔业科学中，对具有年龄结构、随机性较强的种群进行捕捞管理是一个长期且棘手的问题。目前普遍采用基于生物量和捕捞参考点的线性预防性政策，这些政策源自假设生态过程较简单的解析或动态规划解法，常被应用于现实世界中更为复杂的生态情境。本文利用强化学习（RL）和贝叶斯优化工具，研究阿尔伯塔省加拿大鲈鱼（Walleye）渔业中部分可观测、年龄结构化、爆发式繁殖种群的捕捞控制规则设计问题。我们采用多种互补的性能指标优化并评估了各类策略。主要研究问题包括：1. 基于参考点的标准政策与数值优化政策相比表现如何？2. 在仅知种群生物量基础上，加入平均鱼体重观测能否改善决策？结果表明，数值优化策略在高度波动的招募条件下优于传统方法，且引入平均鱼重信息可显著提升政策有效性。
原文摘要 · Abstract (English)
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?
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