# Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems
(2018) studied cooperative tracking strategies and proposed a method based on multi-agent reinforcement learning (MARL). By introducing a parameter-sharing scheme, the proposed method achieved higher hunting rates in a shorter period of time. However, it lacks in-depth discussion on the issue of tracking task allocation, which may hinder maneuverability in the process of multi-target tracking. Regarding the collaborative multi-target pursuit of unmanned surface vehicles (USVs), [PERSON] et al. [PERSON] et al. (2018) modeled the collaborative hunting problem of USV fleets as a decentralized, partially observable Markov decision process. They proposed a distributed partially observable multi-target hunting proximal strategy optimization algorithm suitable for USVs. Experiments have shown that even when certain USVs are damaged, the self-organizing ability of the entire USV fleet still maintains an advantage. The hunting of multiple targets mentioned above is achieved using machine learning methods. The advantage of reinforcement learning is that it does not require establishing a system dynamic model, as it is a model-free method. However, a drawback of machine learning methods is that continuous learning is required to achieve good results, and the learning process consumes a significant amount of time and computing power. Reinforcement learning methods mainly address MASs at the cooperative level, and the information processing between MASs is usually centralized; therefore, it cannot effectively reflect the distributed swarm advantage among agents. Given the shortcomings of reinforcement learning methods and the excellent collaborative ability demonstrated by swarm behavior, this paper proposes a novel trapping strategy based on the research results of the aforementioned literature. In order to solve the problem of hunting multiple dynamic targets that are scattered and escaping, it is necessary to not only design an appropriate hunting strategy but also to consider how to assign the tasks. The result of good task assignment plays a key role in the efficiency of multi-target hunting. [PERSON] et al. [22] studied the distributed allocation algorithm for multi-robots. They proposed two algorithms: the distributed market algorithm and the improved distributed market algorithm. Compared with the centralized Hungarian algorithm, these two algorithms obtained approximate optimal solutions in task allocation, which can effectively reduce the cost of task allocation. [PERSON] et al. [23] further investigated the interactive topology and protocol of task allocation in MASs. In order to achieve interactive communication, an extended contract network protocol, based on point-to-point topology, was proposed, which improved the efficiency and quality of task allocation. [PERSON] et al. [24, 25] investigated coordination behavior strategies for task assignment using a competitive and cooperative approach. Their strategy enables distributed task assignments and ensures the fairness of the tasks. [PERSON] et al. [26] proposed a multi-task allocation algorithm based on a self-organizing map (SOM) for unmanned surface vessels (USVs) to implement complex ocean operations. For the dynamic assignment problem of multi-robots, they used the multi-target optimization method for estimation, in order to achieve optimal task allocation [27]. [PERSON] et al. [28, 29] investigated heuristic algorithms for task assignment, and the results proved that heuristics are efficient, stable, and computationally affordable. This research has studied different task allocation problems from their respective practical application perspectives. However, for the multi-target hunting problem of scattered escape, relying solely on self-organizing task allocation based on limited speed and displacement information may result in too few or no agents around the target, thereby preventing successful capture.