# Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2410.18621 · HTML: https://mlsanity.com/paper/2410.18621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Predator-prey survival pressure is sufficient to evolve swarming behaviors](https://mlsanity.com/paper/2308.12624/) (2023)

## Abstract (arXiv)

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.