Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System
AbstractUnderstanding or estimating the co-evolution processes is critical in ecology, but very challenging. Traditional methods are difficult to deal with the complex processes of evolution and to predict their consequences on nature. In this paper, we use the deep-reinforcement learning algorithms to endow the organism with learning ability, and simulate their evolution process by using the Monte Carlo simulation algorithm in a large-scale ecosystem. The combination of the two algorithms allows organisms to use experiences to determine their behavior through interaction with that environment, and to pass on experience to their offspring. Our research showed that the predators’ reinforcement learning ability contributed to the stability of the ecosystem and helped predators obtain a more reasonable behavior pattern of coexistence with its prey. The reinforcement learning effect of prey on its own population was not as good as that of predators and increased the risk of extinction of predators. The inconsistent learning periods and speed of prey and predators aggravated that risk. The co-evolution of the two species had resulted in fewer numbers of their populations due to their potentially antagonistic evolutionary networks. If the learnable predators and prey invade an ecosystem at the same time, prey had an advantage. Thus, the proposed model illustrates the influence of learning mechanism on a predator–prey ecosystem and demonstrates the feasibility of predicting the behavior evolution in a predator–prey ecosystem using AI approaches. View Full-Text
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Wang, X.; Cheng, J.; Wang, L. Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System. Entropy 2019, 21, 773.
Wang X, Cheng J, Wang L. Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System. Entropy. 2019; 21(8):773.Chicago/Turabian Style
Wang, Xueting; Cheng, Jun; Wang, Lei. 2019. "Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System." Entropy 21, no. 8: 773.
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