Control theory and stochastic dynamic programming have long been used to develop optimal single-species management policies. However, most species interact with others through competition and predation as parts of complex ecosystems. As a consequence, it is unclear how far from optimal the single species policies currently in use actually are. Moreover, there are as yet no scalable algorithms for optimal ecosystem management.

Here, we merge recently developed tools from machine learning and nonlinear dynamics to construct and evaluate near-optimal policies in multi-species systems. Specifically, a non-parametric model for the dynamics is estimated from time series data using Gaussian process-based dynamic modeling. A policy is then derived from the inferred dynamics using a temporal difference learning algorithm. Policy performance is benchmarked against single-species policies and the ad hoc ecosystem policies that have been previously offered. We found that EDP policies are closer to the true optimal policies than single-species policies in multi-species systems with two controls and three objectives. The Pareto fronts illustrate the flexibility of EDP policies compared with single-species policies.

控制理论和随机动态规划长期以来一直用于制定最佳的单物种管理策略。但是，大多数物种通过竞争和捕食作为复杂生态系统的一部分与其他物种相互作用。结果，目前尚不清楚当前使用的单一物种政策离最佳策略还有多远。而且，还没有用于优化生态系统管理的可扩展算法。

在这里，我们将机器学习和非线性动力学中最近开发的工具进行合并，以构建和评估多物种系统中的近最优策略。具体而言，使用基于高斯过程的动态建模从时间序列数据中估计动力学的非参数模型。然后，使用时间差异学习算法从推断的动态中得出策略。政策绩效以单一物种政策和先前提供的临时生态系统政策为基准。我们发现，在具有两个控制和三个目标的多物种系统中，EDP策略比单物种策略更接近真正的最优策略。帕累托阵线说明了与单一物种政策相比，EDP政策的灵活性。