# Adaptive-Modeling Multi-Agent Learning System for Video Behavioral Clustering Recognition
## 2 Related Work  
Mainstream multi-agent systems use reinforcement learning for policy optimization. Moreover, most use centralized training and decentralized execution (CTED) approaches, such as MADDPG and QMIX, where the learning process is centralized and relies on labeled datasets to proceed with offline learning. For an agent evaluation problem such as a credit assignment, Google's [PERSON] et al. (2018) proposed a novel value-decomposition network architecture to solve the \"lazy agent\", making each agent a learning goal of its own, and the overall contribution of the team is the sum of individual contributions. [PERSON] and [PERSON] (2018) introduced the attention mechanism on the basis that agents need to pay more attention to those agents that require attention while learning, allowing the agents' attention to be differentiated. [PERSON] et al. (2018) propose an evolutionary population curriculum to solve the problem of excessive learning complexity as the number of agents increases. However, the centralized training approach still limits the learning freedom of individuals, and it also requires the manual setting of global evaluation criteria for all agent behavior, limiting the dynamics of the system.  
In video processing, the optical flow method is a commonly used computer vision technique for estimating pixel motion from image sequences. For example, FlowNet (2018; 2019) uses a convolutional neural network to solve the optical flow estimation problem. Moreover, Two-Stream (2018; 2019; 2019) uses two classifiers, one trained for RGB graphs and one trained for optical flow graphs, and then the results of both are subjected to a fusion process. However, the optical flow method is computationally demanding and slow owing to its high density computation. In unsupervised video learning, DPC (2019) slices video into nonrepeating blocks by intensive computation and thus predicts future frames. MoAp (2019) is similar to our design concept, which is based on the idea that the human visual system is based on a series of learned patterns for computing the exact statistics of the data. Additionally, deep neural embeddings (2019) improve the video instance embedding framework by capturing frames from videos and then composing video embedding from picture embedding to achieve clustering. However, the unsupervised learning approach requires multiple frames of interest to be found autonomously, thus making the processing task computationally intensive. Inspired by traditional optical flow (2019; 2019) and MP3 (2020), we chose to use a background-based modeling approach to convert video frames using filtering to discrete sequence coding in a binary encoding, which leads to a loss of accuracy and detail but enables a focus on the changing patterns of the motion process. In the application of recognizing video-level motion processes, binary encoding methods can satisfy clustering operations by focusing on the change process without any impact from the loss of precise RGB information and object details. Furthermore, in the context of video behavioral clustering recognition scenarios, density-based spatial clustering of applications with noise (DBSCAN) (2019) and hierarchical agglomerative clustering (2020) are two equally applicable algorithmic models. DBSCAN is a well-known density-based clustering algorithm suitable for clustering analysis of lower-dimensional data, while hierarchical performs multilayer clustering in a treelike structure. Both algorithms have high clustering recognition rates, averaging above 90%.