Collaborative multi-agent reinforcement learning based on a novel coordination tree frame with dynamic partition
We present a cooperation tree-structure by using the subset of cooperation agents as the nodes of a tree. Two kind of weights are defined which describe the cost of an agent collaborating with or without an agent subset respectively. Each agent calculates its collaborative agent subset with a minimal cost based on coordination trees.
multi-agent coordination tree markov games belief propagation q learning