基于强化学习的多发导弹协同攻击智能制导律

A reinforcement learning-based cooperative guidance law utlitizing a deep deterministic policy gradient descent neural network is proposed to achieve the cooperative attack of multiple missiles against a target and improve the attack effectiveness. The estimation equation of time-to-go based on the linear engagement dynamics is revised to improve the estimation accuracy of time-to-go, which is no longer restricted by the assumption of small angle. The time-to-go error of each missile is regarded as the coordination variable. The time-to-go error and range-to-go of each missile are used as the observables of the reinforcement learning algorithm. The reward function is constructed by using miss distance and time-to-go error, and then a reinforcement learning agent is generated by offline training. In the process of closed-loop guidance, the reinforcement learning agent generates guidance commands in real time, by that simultaneous attack can be achieved. Simulated results verify that the proposed reinforcement learning guidance law can achieve simultaneous attack on the target. Compared with the traditional cooperative guidance law, the reinforcement learning cooperative guidance law can be used to obtain smaller miss distances and smaller attack time errors.