Multi-Agent Cooperative Pursuit Based on Permutation Invariance and Task Decomposition

Multi-agent pursuit-evasion has significant applications in military, transportation, and industrial sectors. This task faces dual challenges of uncertain swarm scale and environmental uncertainty within unstructured dynamic environments. To address these, we propose a hierarchical reinforcement learning framework based on permutation invariance to balance pursuit efficiency and collision avoidance safety. First, in the evaluation stage, we introduce a hybrid feature aggregation mechanism based