This is the first paper (in review) in the Multi-agent learning in dynamic systems series. Developing new algorithms to optimise task allocation in multi-agent systems. Q-learning, historical reward convolution, and dynamically adaptable risk-based system exploration approaches are developed.

As the pace of progress within existing markets increases year on year there are both new opportunities and substantial challenges to the successful and established companies across many industries. One of the key pathways through this modern landscape is often summed up in the all-encompassing term ‘Digital Transformation’. The definition varies depending on who you ask, but broadly speaking is a mix of modernisation of IT infrastructure and movement to more Agile methodologies. In…

This is the introductory poster for the Multi-agent learning in dynamic systems series. In systems with a large number of agents there are fundamental pressures on the centralised coordination techniques used to provide inter-communication, task orchestration, and routing of messages. As the scale of interacting components expands, we reach resource constraint plateaus, where computation, storage, or communication pathways become saturated. At these points we must decompose each agents…