Adaptive Communication Topology for Resilient Multi-Agent Coordination Using Graph Neural Networks

An adaptive communication topology for resilient multi-agent coordination using graph neural networks.

An adaptive communication topology for resilient multi-agent coordination using graph neural networks. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

The main parts are cooperating agents with defined roles and tools, dataset preparation and augmentation, model design and training, evaluation with standard metrics, plus error analysis, and a simple interface or API to try the model. The reference build uses LLM APIs, PyTorch Geometric, Python, PyTorch, and TensorFlow, with a clear project structure, setup instructions and sample data so that it can be run and extended.