  An approach of mobile multi-agent pursuit based on application of
self-organizing feature map (SOFM) and along with that reinforcement learning
based on agent group role membership function (AGRMF) model is proposed. This
method promotes dynamic organization of the pursuers' groups and also makes
pursuers' group evader according to their desire based on SOFM and AGRMF
techniques. This helps to overcome the shortcomings of the pursuers that they
cannot fully reorganize when the goal is too independent in process of AGRMF
models operation. Besides, we also discuss a new reward function. After the
formation of the group, reinforcement learning is applied to get the optimal
solution for each agent. The results of each step in capturing process will
finally affect the AGR membership function to speed up the convergence of the
competitive neural network. The experiments result shows that this approach is
more effective for the mobile agents to capture evaders.
