Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we study a method to deal with a reduction in the number of agents in the training process without changing the structure of the neural network (NN), which is called the frozen agent method for the MADDPG (FA-MADDPG) algorithm.
However, the existing algorithms cannot deal with the situation where the number of agents reduces.
In various situations, the agents of both sides may crash due to collisions. The multi-agent deep reinforcement learning (MADRL) method is applied in similar scenarios to help agents make decisions. In the multi-agent offensive and defensive game (ODG), each agent achieves its goal by cooperating or competing with other agents. 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. 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. These courses are used to train air combat agents respectively, and compared with the original method without any curriculum. First, three curricula of air combat maneuver decision-making are designed: angle curriculum, distance curriculum and hybrid curriculum. In order to solve these problems, the method based on curriculum learning is proposed. 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. It is a meaningful and valuable direction to investigate autonomous air combat maneuver decision-making method based on reinforcement learning. Reinforcement learning is an effective way to solve the decision-making problems.
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