Abstract Classic population models can often predict the dynamics of biological populations in nature. However, the adaptation process and learning mechanism of species are rarely considered in the study of population dynamics, due to the complex interaction of species, seasonal variation, spatial distribution or other factors. We use reinforcement learning algorithms to improve the existing individual-based ecosystem simulation algorithms, which allows species to spontaneously adjust their strategies according to a short period of experience and then feed back to improve their abilities to make action decisions. Our results show that the reinforcement learning of predators is beneficial to the stability of the ecosystem, and predators can learn to spontaneously form hunting patterns that surround their prey. The learning of prey makes the ecosystem oscillate and meanwhile leads to a higher risk of extinction for predators. When individuals are more likely to die, these herbivores rely on reproductive behavior to maintain their populations; when individuals live longer, herbivores spend more time eating to maintain their own survival. The co-reinforcement learning of predators and prey helps predators to find a more suitable way to survive with their prey, that is, the number of predators is more stable and larger than when only predator or only prey learns.

中文翻译：

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基于强化学习的捕食者-猎物模型

摘要 经典种群模型通常可以预测自然界中生物种群的动态。然而，由于物种之间复杂的相互作用、季节变化、空间分布或其他因素，在种群动态研究中很少考虑物种的适应过程和学习机制。我们使用强化学习算法来改进现有的基于个体的生态系统模拟算法，它允许物种根据短时间内的经验自发调整其策略，然后反馈以提高其做出行动决策的能力。我们的研究结果表明，捕食者的强化学习有利于生态系统的稳定，捕食者可以学会自发形成围绕猎物的狩猎模式。对猎物的学习使生态系统振荡，同时导致捕食者灭绝的风险更高。当个体更有可能死亡时，这些食草动物依靠繁殖行为来维持种群；当个体寿命更长时，食草动物会花更多时间进食以维持自身的生存。捕食者和猎物的协同强化学习有助于捕食者找到更适合与猎物一起生存的方式，即捕食者的数量比只有捕食者或只有猎物学习时更稳定、更大。