The application discloses a kind of distributed edge collaborative video analysis methods based on multi-agent
reinforcement learning, through mutual cooperation of edge nodes, jointly learn the optimal strategy of video frame pre-
processing,
model selection and request scheduling, to minimize the overall cost of
system.The application models each
edge node as an agent, which is an autonomous entity and makes distributed control decisions by observing its local state.The application uses an attention mechanism to distinguish the importance of information collected from different edge nodes, and its performance is verified by deploying a video analysis
test platform with multiple edge nodes, and extensive experiments are conducted using real-world datasets and experimental settings.The experimental results show that compared with existing baseline methods, the application can significantly improve the overall reward by 33.6%-86.4%.