This is the second paper (in review) in the Multi-agent learning in dynamic systems series. This work looks at how agents can learn to allocate restricted resources to handle incoming tasks, adapting to meet the goals of other agents as they gain knowledge on those goals.

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.