emergent_communication_in_agents

Repository accompanying the paper Emergent communication enhances foraging behaviour in evolved swarms controlled by Spiking Neural Networks

Figure 1 of the manuscript. A multi-agent system (ant colony) steered by spiking neural networks (SNNs) is foraging for food. The ants in red are exploring the environment and return found food to the nest. White/blue patches indicate the pheromone concentration. The food piles are depicted as green patches and leafs. The hexagon in the middle is the nest.

Requirements to run the simulations

To run the simulations please fist follow the installation requirements specified for L2L, see https://github.com/Meta-optimization/L2L. Additionally, NEST 3.3 and NetLogo 6.3 are needed. We run the simulations with Python 3.9. To plot the figures (see Data and figure guide), the plotting library Seaborn 0.12.0 is required. After cloning and installing L2L, place the file under bin/l2l-neuroevolution_multi_ant.py into the bin folder of L2L. Execute python l2l-neuroevolution_multi_ant.py for a local run.