Decision Modeling and Simulation of Fighter Air-to-ground Combat Based on Reinforcement Learning

Abstract

With the Artificial Intelligence (AI) widely used in air combat simulation system, the decision-making system of fighter has reached a high level of complexity. Traditionally, the pure theoretical analysis and the rule-based system are not enough to represent the cognitive behavior of pilots. In order to properly specify the autonomous decision-making of fighter, hence, we proposed a unified framework which combines the combat simulation and machine learning in this paper. This framework adopts deep reinforcement learning modelling by using the supervised learning and the Deep Q-Network (DQN) methods. As a proof of concept, we built an autonomous decision-making training scenario based on the Weapon Effectiveness Simulation System (WESS). The simulation results show that the intelligent decision-making model based on the proposed framework has better combat effects than the traditional decision-making model based on knowledge engineering.