Park Jeongho, Lee Juwon, Kim Taehwan, Ahn Inkyung, Park Jooyoung
Department of Control and Instrumentation Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30019, Korea.
Department of Mathematics, College of Science and Technology, Korea University, 2511 Sejong-ro, Sejong-City 30019, Korea.
Entropy (Basel). 2021 Apr 13;23(4):461. doi: 10.3390/e23040461.
The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their complex interactions, the study of population dynamics still remains a challenging task in computational biology. In this paper, we use a modern deep reinforcement learning (RL) approach to explore a new avenue for understanding predator-prey ecosystems. Recently, reinforcement learning methods have achieved impressive results in areas, such as games and robotics. RL agents generally focus on building strategies for taking actions in an environment in order to maximize their expected returns. Here we frame the co-evolution of predators and preys in an ecosystem as allowing agents to learn and evolve toward better ones in a manner appropriate for multi-agent reinforcement learning. Recent significant advancements in reinforcement learning allow for new perspectives on these types of ecological issues. Our simulation results show that throughout the scenarios with RL agents, predators can achieve a reasonable level of sustainability, along with their preys.
在生态学中寻找合适的种群模型问题，对于理解其动态本质的关键方面至关重要。由于多种物种间复杂的相互作用使得分析和准确预测其智能适应性颇具难度，种群动态研究在计算生物学领域仍是一项具有挑战性的任务。在本文中，我们采用现代深度强化学习（RL）方法来探索理解捕食者 - 猎物生态系统的新途径。近来，强化学习方法在诸如游戏和机器人技术等领域取得了令人瞩目的成果。RL智能体通常专注于构建在环境中采取行动的策略，以最大化其预期回报。在此，我们将生态系统中捕食者与猎物的共同进化构建为允许智能体以适合多智能体强化学习的方式学习并朝着更好的方向进化。强化学习领域最近的重大进展为这类生态问题带来了新视角。我们的模拟结果表明，在整个有RL智能体参与的场景中，捕食者及其猎物都能实现合理水平的可持续性。
