Adaptive coordination among fuzzy reinforcement learning agents performing distributed dynamic load balancing

We present an adaptive multi-agent coordination algorithm applied to the problem of distributed dynamic load balancing. As a specific example, we consider the problem of dynamic web caching in the Internet. In our general formulation of this problem, each agent represents a mirrored piece of content that tries to move itself closer to areas of the network with a high demand for this item. Each agent in our model uses a fuzzy rule base for choosing the optimal direction of motion and adjusts the parameters of this rule base using reinforcement learning. The resulting architecture for multi-agent coordination among fuzzy reinforcement learning agents (MAC-FRL) allows the team of agents to adaptively redistribute its members in the environment to match the changing pattern of demand. We simulate the performance of MAC-FRL and show that it significantly improves performance over non-coordinating agents