Guidance law based on deep Q network algorithm

Abstract

In this paper, an intelligent guidance law based on Deep Q Network (DQN) algorithm is proposed, for enabling the missile to intercept different maneuvering targets following the idea of the parallel-approach method. In specific, we propose the inverse ratio of the absolute value of line-of-sight (LOS) angle rate as the shaping reward function which guarantees the successful finding of the control strategy and the speeding up of the training process of the reinforcement learning (RL) model. Furthermore, to avoid rapid chattering caused by directly choosing missile acceleration as an action, we introduce the change rate of the acceleration as the action in the DQN algorithm and integrate it to obtain the acceleration command. Therefore, in our algorithm only LOS angle, LOS angle rate, and missile overload information are used in the established RL model to generate the guidance command, which is easy to implement. The simulation results and comparative experiments demonstrate that the proposed RL based guidance method achieves better guidance accuracy and higher success rate. The great performance of the proposed method suggests that the RL based guidance method is promising for the maneuvering target, and deserve further investigations in future.