Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning
This work addresses species dynamics prediction for ecology, but it is incremental as it builds on existing starvation-driven diffusion models with reinforcement learning.
The study tackled the challenge of accurately predicting species dispersal in heterogeneous environments by using multi-agent deep reinforcement learning to simulate evolutionary dispersal strategies, revealing insights into species dispersal mechanisms and validating traditional mathematical models.
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.