用强化学习模拟物种在复杂环境中的演化扩散策略。
Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning
- 基于饥饿驱动扩散机制，用多智能体深度Q网络模拟物种迁移
- 揭示了物种在资源不均环境中演化出的最优扩散行为
- 适合生态建模、智能体学习交叉领域的研究者参考
理解异质环境中的物种动态对生态系统研究至关重要。传统模型假设生境均一，而近期方法引入空间与时间变异，强调物种迁徙的作用。本文采用饥饿驱动扩散（SDD）模型作为非线性扩散描述，基于局部资源条件刻画物种扩散，展现出促进生存的优势。然而，由于模型简化，准确预测仍具挑战。本研究利用多智能体强化学习（MARL）结合深度Q网络（DQN），模拟单物种及捕食者-猎物互动，引入类SDD奖励机制。仿真结果揭示了物种演化的扩散策略，为物种扩散机制提供新见解，并验证了传统数学模型的有效性。
原文摘要 · Abstract (English)
Understanding species dynamics in heterogeneous environments is essential for ecosystem studies. Traditional models assumed homogeneous habitats, but recent approaches include spatial and temporal variability, highlighting species migration. We adopt starvation-driven diffusion (SDD) models as nonlinear diffusion to describe species dispersal based on local resource conditions, showing advantages for species survival. However, accurate prediction remains challenging due to model simplifications. This study uses multi-agent reinforcement learning (MARL) with deep Q-networks (DQN) to simulate single species and predator-prey interactions, incorporating SDD-type rewards. Our simulations reveal evolutionary dispersal strategies, providing insights into species dispersal mechanisms and validating traditional mathematical models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。