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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.18621 |
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| _version_ | 1866910664736374784 |
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| author | Choi, Wonhyung Ahn, Inkyung |
| author_facet | Choi, Wonhyung Ahn, Inkyung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning Choi, Wonhyung Ahn, Inkyung Populations and Evolution Machine Learning Dynamical Systems 35J60, 35K57, 92D25, 68T05, 93E35 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. |
| title | Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning |
| topic | Populations and Evolution Machine Learning Dynamical Systems 35J60, 35K57, 92D25, 68T05, 93E35 |
| url | https://arxiv.org/abs/2410.18621 |