Continuous Decision-making Method for Autonomous Air Combat

Abstract

The future air warfare is developing in the unmanned and autonomous direction.The autonomous air warfare decision-making methods are one of the important support methods in future.Due to dimensional limitations,traditional air combat decision-making methods cannot handle continuous action and long-sighted decision-making problems.Based on the Actor-Critic method,a unified architecture for continuous decision-making in air combat is proposed in this paper.Combining air combat training experience,the state space,action space,reward and training subjects are rationally designed,and a variety of continuous action space reinforcement learning algorithms are tested in high uncertainty.The learning effect in the air combat scenario is visually verified.The results show that:based on the method architecture proposed in this paper,long-sighted value optimization under continuous actions can be realized,the agent can make optimal decisions in complex air combat situations,and has a high kill rate against random maneuvering flying targets.And the air combat maneuver trajectory is highly reasonable.

Keywords

• autonomous air combat
• reinforcement learning
• artificial intelligence
• deep neural network