Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards
This addresses training inefficiencies for autonomous air combat systems, but it is incremental as it builds on existing reinforcement learning methods with a specific curriculum adaptation.
The authors tackled the problem of slow training and poor performance in autonomous air combat maneuver decision-making using reinforcement learning with sparse rewards by proposing a curriculum learning approach, showing that angle curriculum increased training speed and stability while improving agent performance, whereas hybrid curriculum had negative effects.
Reinforcement learning is an effective way to solve the decision-making problems. It is a meaningful and valuable direction to investigate autonomous air combat maneuver decision-making method based on reinforcement learning. However, when using reinforcement learning to solve the decision-making problems with sparse rewards, such as air combat maneuver decision-making, it costs too much time for training and the performance of the trained agent may not be satisfactory. In order to solve these problems, the method based on curriculum learning is proposed. First, three curricula of air combat maneuver decision-making are designed: angle curriculum, distance curriculum and hybrid curriculum. These courses are used to train air combat agents respectively, and compared with the original method without any curriculum. The training results show that angle curriculum can increase the speed and stability of training, and improve the performance of the agent; distance curriculum can increase the speed and stability of agent training; hybrid curriculum has a negative impact on training, because it makes the agent get stuck at local optimum. The simulation results show that after training, the agent can handle the situations where targets come from different directions, and the maneuver decision results are consistent with the characteristics of missile.