Articles

Reinforcement learning for emergent multi-agent systems

Glad to say that my thesis has passed and been accepted by Kings College London, and I am now a Doctor of Philosophy in Multi-agent Systems! For anyone interested in “Agent-based self-organisation for task allocation: reinforcement learning for emergent multi-agent systems”, here is the final work. This brings together research work and new algorithms for tackling task and resource allocation in multi-agent systems using reinforcement learning, as well as the emergence of roles within an agent-based system through self-organisation driven by inter-agent rewards and knowledge exchange. If anyone has any interesting work going on in this or any related areas, feel free to drop me a message.

Resource allocation in dynamic multiagent systems

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. Multi-agent learning in dynamic systems Focused on applying reinforcement learning techniques to multi-agent systems where the environment is dynamic, and realistic resource constraints exist. This work combines task-allocation optimisation, resource allocation, and self-organising hierarchical agent structures.

Multi-agent task allocation using reinforcement learning

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. Multi-agent learning in dynamic systems Focused on applying reinforcement learning techniques to multi-agent systems where the environment is dynamic, and realistic resource constraints exist. This work combines task-allocation optimisation, resource allocation, and self-organising hierarchical agent structures.

Self-organisation of autonomous intelligent agents

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 functionality into a number of specialisms that can then be taken up by other agents, at the cost of even more orchestration communications and synchronisation to provide this distributed functionality. To provide solutions to tackle these issues we focus on distributed agent systems where reinforcement learning behaviours are constrained by resource usage limits and hence by local neighbourhood awareness rather than global system knowledge. Multi-agent learning in dynamic systems Focused on applying reinforcement learning techniques to multi-agent systems where the environment is dynamic, and realistic resource constraints exist. This work combines task-allocation optimisation, resource allocation, and self-organising hierarchical agent structures.

Drafts