Multi-Agent Air Combat Decision-making Based on Battlefield Attention Information

With the rapid development of artificial intelligence and neural networks, deep reinforcement learning has achieved remarkable results in a series of complex sequential decision-making problems. The application of multi-agent reinforcement learning in air combat game scenarios is also booming. In the use of reinforcement learning for multi-agent air combat decision-making, the scalability and transferability of the model have become critical issues. Designing a multi-agent air combat decision-making framework with solid scalability, robustness, and rapid convergence has become a research hotspot in various countries. To address this problem, this paper proposes a multi-agent air combat decision-making framework based on attention mechanism transfer and designs a 2D air combat simulation environment for this framework. The decision-making process of this framework is divided into two stages. First, course learning is carried out in the designed essential air combat environment to enhance the aircraft's combat capability. Then, the trained strategy is transferred to a complex air combat environment for further training. Experiments have shown that this framework has better transferability and robustness.