Missile-Target Situation Assessment Model Based on Reinforcement Learning

Abstract

In situation assessment (SA) of missile versus target fighter, the traditional SA models generally have the characteristics of strong subjectivity and poor dynamic adaptability. This paper considers SA as an expectation of future returns and establishes a missile-target simulation battle model. The actor-critic (AC) algorithm in reinforcement learning (RL) is used to train the evaluation network, and a missile-target SA model is established in simulation battle training. Simulation and comparative experiments show that the model can effectively estimate the expected effect of missile attack under the current situation, and it provides an effective basis for missile attack decision.