A MARL-Based Approach to Intelligent Strategic Decision-Making for Air-Sea Confrontation

To solve the problem of long OODA cycle time and untimely decision-making by traditional decision-making means in air-sea confrontation and better comprehend advanced combat concepts worldwide, we propose a multi-agent reinforcement learning algorithm that can make decisions regarding our combat formations’ operations. The algorithm's state space, action space, and reward function are designed for a comprehensive battlefield situation, enabling it to solve the top-level decision-making problem for sea-air confrontation. The tactical execution of the decision-making results uses a military chess deduction platform to create a complete decision-making command pathway. Finally, we have conducted experiments to test the algorithm’s effectiveness and generalisation capability in an air-sea confrontation scenario.