Wang Xueting, Cheng Jun, Wang Lei
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin 999077, Hong Kong, China.
Entropy (Basel). 2019 Aug 8;21(8):773. doi: 10.3390/e21080773.
Understanding 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.
理解或估计协同进化过程在生态学中至关重要，但极具挑战性。传统方法难以处理复杂的进化过程，也难以预测其对自然界的影响。在本文中，我们使用深度强化学习算法赋予生物体学习能力，并在大规模生态系统中利用蒙特卡罗模拟算法模拟它们的进化过程。这两种算法的结合使生物体能够通过与环境的交互利用经验来决定其行为，并将经验传递给后代。我们的研究表明，捕食者的强化学习能力有助于生态系统的稳定，并帮助捕食者获得与其猎物共存的更合理行为模式。猎物对自身种群的强化学习效果不如捕食者，且增加了捕食者灭绝的风险。猎物和捕食者不一致的学习周期和速度加剧了这种风险。由于它们潜在的对抗性进化网络，这两个物种的协同进化导致了它们种群数量的减少。如果可学习的捕食者和猎物同时侵入一个生态系统，猎物具有优势。因此，所提出的模型说明了学习机制对捕食者 - 猎物生态系统的影响，并证明了使用人工智能方法预测捕食者 - 猎物生态系统中行为进化的可行性。
