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.
Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number
Università “Sapienza”（罗马第一大学）；University of Rome Tor Vergata（罗马第二大学）；Istituto dei Sistemi Complessi, CNR（意大利国家研究委员会复杂系统研究所）；The Abdus Salam International Centre for Theoretical Physics - ICTP（阿卜杜勒·萨拉姆国际理论物理中心）
Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games
Robotics, Perception and Learning Lab., School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH)（机器人感知与学习实验室，电气工程与计算机科学学院，皇家理工学院（KTH））
Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Dalian University of Technology（大连理工大学）；Università della Svizzera italiana（瑞士意大利大学）；Politecnico di Milano（米兰理工学院）
