UAV Cooperative Air Combat Maneuvering Confrontation Based on Multi-agent Reinforcement Learning
Zihao Gong, Yang Xu, Delin Luo
Abstract
Focusing on the problem of multi-UAV cooperative air combat decision-making, a multi-UAV cooperative maneuvering decision-making approach is proposed based on multi-agent deep reinforcement learning (MARL) theory. First, the multi-UAV cooperative short-range air combat environment is established. Then, by combining the value-decomposition networks (VDNs) deep reinforcement learning theory with the embedded expert collaborative air combat experience reward function, an air combat cooperative strategy framework is proposed based on the networked decentralized partially observable Markov decision process (NDec-POMDP). The air combat maneuvering strategy is then optimized to improve the cooperative degree between UAVs in cooperative combat scenarios. Finally, multi-UAV cooperative air combat simulations are carried out and the results show the feasibility and effectiveness of the proposed cooperative air combat decision-making framework and method.