Vaughan, Neil2018-10-102018-10-102018-07-07Vaughan, N. (2018) Multi-agent reinforcement learning for swarm retrieval with evolving neural network. In V. Vouloutsi, et al. (Eds.) Biomimetic and Biohybrid Systems 7th International Conference, Living Machines, Paris, France, July 17–20, 2018.978331995971910.1007/978-3-319-95972-6_56http://hdl.handle.net/10034/621474The final publication is available at Springer via https://doi.org/10.1007/978-3-319-95972-6_56This research investigates methods for evolving swarm communica-tion in a sim-ulated colony of ants using pheromone when foriaging for food. This research implemented neuroevolution and obtained the capability to learn phero-mone communication autonomously. Building on previous literature on phero-mone communication, this research applies evolution to adjust the topology and weights of an artificial neural network (ANN) which controls the ant behaviour. Compar-ison of performance is made between a hard-coded benchmark algorithm (BM1), a fixed topology ANN and neuroevolution of the ANN topology and weights. The resulting neuroevolution produced a neural network which was suc-cessfully evolved to achieve the task objective, to collect food and return it to a location.enhttps://creativecommons.org/licenses/by-nc-nd/3.0/swarm communicationNeuroevolutionArtificial AntsConference Contribution