# Adaptive-Modeling Multi-Agent Learning System for Video Behavioral Clustering Recognition
## 1 Introduction  
Multi-agent systems are extremely adaptable in dealing with complex problems. These systems have since been combined with reinforcement learning algorithms, enabling even more research progress [1, 2]. Independent training approaches, such as independent Q-learning (IQL) [3], suffer from difficulty in achieving learning convergence due to the shared dynamic instability environment factors, while centralized execution, such as value-decomposition networks (VDNs) [4], integrates the value function of each agent, improving performance by ignoring individual characteristics to a greater extent. Most multi-agent systems use the centralized training and decentralized execution (CTED) approach, such as MADDPG [5] and QMIX [6]. However, centralized training usually evaluates the agents and computational tasks based on the system goal in a multi-agent system. In some cases, learning takes place in a simulator or laboratory [7], where agents have access to additional state information and can communicate freely through global data sharing. In contrast, the decentralized execution approach uses a strategy of decentralizing the computational tasks, where each agent performs its learning process with local data and the environment. The decentralized approach also reduces the complexity of interaction with the increased number of agents and avoids partial observability challenges. Therefore, finding an effective decentralized execution strategy is important for both training and execution. In addition, the process of changing the visual information of the video is always continuous and connected to the temporal task. If the frame is divided into several regions, the changes in different regions over time will show a clear regularity. The regularities of these changes can be summarized in higher dimensions. Therefore, the frame's change can be summarized in higher dimensions by focusing on the dynamic situations of many small regions. The law enables encoding of changes in different regional pictures to obtain temporally related but different temporal encoding sequences. Real-time clustering recognition requires neighboring regions at the same level to form a new sequence based on discrete temporal sequences (DTSs). A multilayer structure for clustering recognition can cover the global video frame and learn from the summary of small change patterns to achieve global behavioral recognition. A multi-agent system is a computing system comprising multiple autonomous entities that interact with each other to achieve certain goals. Each agent possesses its own objectives, knowledge, and decision-making capability and can effectively handle parallel tasks. More importantly, clustering of video behavior would be a very complex task if only regular modeling was used, whereas a multi-agent approach can segment the frame to effectively reduce the computational complexity of each agent, and using a multilayer agent system can cover the entire frame, achieving learning effects from the recognition of small regions to global behavioral clustering. Thus, based on the decentralization strategy and the continuity of the encoding of movement information, by using traditional methods rather than the neural network or reinforcement learning, this paper proposes a memory-like adaptive-modeling multi-agent learning system, and it makes the main contributions that follow:  
1. The design system handles video-level behavioral recognition, encoding video information as a discrete temporal sequence as a means of processing data. The design uses a multilayer structure, and the clustering results of agents form a new DTS at higher-layer agents, forming a self-organizing structural pattern in the bottom-up direction;
2. The design agents all use prediction as the evaluation criterion, so they are only concerned with local performance. The top-layer agent only cares about its own execution, but it also represents the performance of the system, and its clustering result is expressed as the recognition result of the video behavior;
3.