论文标题
多代理合作追求捕获分组逃生的新方法
A novel approach for multi-agent cooperative pursuit to capture grouped evaders
论文作者
论文摘要
提出了基于自组织特征图（SOFM）的应用以及基于代理人组角色成员资格功能（AGRMF）模型的强化学习的移动多机构追求方法。这种方法促进了追随者群体的动态组织，并根据SOFM和AGRMF技术的愿望使追捕者的逃生者成为逃生者。这有助于克服追随者的缺点，即当目标在AGRMF模型操作过程中实现的目标太独立时，他们无法完全重组。此外，我们还讨论了一个新的奖励功能。组形成后，应用增强学习以为每个代理提供最佳解决方案。捕获过程的每个步骤的结果最终将影响AGR成员功能，以加快竞争性神经网络的收敛性。实验结果表明，这种方法对移动药物捕获逃生者更有效。
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.
