A reinforcement learning-based predator-prey model
2020
Wang, Xueting | Zheng, Run | Wang, Lei
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.
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