自己組織化マップと強化学習による群れ追跡者の協調捕獲手法
A novel approach for multi-agent cooperative pursuit to capture grouped evaders
SOFMと強化学習(AGRMFモデル)を組み合わせ、複数の追跡エージェントが群れをなす獲物を効率的に捕獲するための協調制御手法を提案した論文。
著者: Muhammad Zuhair Qadir, Songhao Piao, Haiyang Jiang, Mohammed El Habib Souidi
分類: cs.AI, cs.GT, cs.LG, cs.MA, cs.RO
原文アブストラクト
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.
