سال انتشار:
2020
عنوان انگلیسی مقاله:
A reinforcement learning-based predator-prey model
ترجمه فارسی عنوان مقاله:
مدل درنده شکار مبتنی بر یادگیری تقویتی
منبع:
Sciencedirect - Elsevier - Ecological Complexity, 42 (2020) 100815: doi:10:1016/j:ecocom:2020:100815
نویسنده:
Xueting Wanga,b, Jun Cheng⁎,a,b, Lei Wanga,b
چکیده انگلیسی:
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.
Keywords: Q-learning | Monte Carlo simulation | Population dynamics
قیمت: رایگان
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