Abstract This paper discussed the cooperative control problem for swarming systems in unknown dynamic environment. The swarm agents are required to move in a completely distributed manner with the reference trajectory determined by a virtual dynamic leader. In addition to keeping an appropriate distance from neighboring agents, each agent needs to avoid collision with dynamic threats in unknown environment. All of these complex requirements are integrated and designed as the performance index function for each agent. Then, the cooperative learning behavior of swarming system is realized by applying the reinforcement learning theory. Neural networks are used to model the control scheme and trained to minimize the performance index. The online updating rules of the neural networks are achieved based on the gradient descent algorithm. Finally, two simulation experiments are performed to verify the effectiveness of the cooperative control scheme and the environmental adaptability of the swarm agents.

中文翻译：

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未知动态环境下基于强化学习的集群系统协同控制

摘要 本文讨论了未知动态环境下集群系统的协同控制问题。群代理需要以完全分布式的方式移动，参考轨迹由虚拟动态领导者确定。除了与相邻代理保持适当的距离外，每个代理还需要避免与未知环境中的动态威胁发生碰撞。所有这些复杂的需求都被集成并设计为每个代理的性能指标函数。然后，应用强化学习理论实现集群系统的合作学习行为。神经网络用于对控制方案进行建模并进行训练以最小化性能指标。神经网络的在线更新规则是基于梯度下降算法实现的。最后，