In a competition with all opponents, the winning rate of the strategic agent selected by the league system is more than 44%, and the probability of not losing is about 75%. For the training of an opponent with the adaptive strategy, the winning rate can reach more than 50%, and the losing rate can be reduced to less than 15%. Simulation results show that the proposed approach can be applied to maneuver guidance in air combat, and typical angle fight tactics can be learnt by the deep reinforcement learning agents. A league system is adopted to avoid the red queen effect in the game where both sides implement adaptive strategies. Agents are trained by alternate freeze games with a deep reinforcement algorithm to deal with nonstationarity. ![]() A reward shaping approach is used, by which the training speed is increased, and the performance of the generated trajectory is improved. ![]() Middleware which connects the agents and air combat simulation software is developed to provide a reinforcement learning environment for agent training. ![]() The maneuver strategy agents for aircraft guidance of both sides are designed in a flight level with fixed velocity and the one-on-one air combat scenario. In this paper, an alternate freeze game framework based on deep reinforcement learning is proposed to generate the maneuver strategy in an air combat pursuit. In a one-on-one air combat game, the opponent’s maneuver strategy is usually not deterministic, which leads us to consider a variety of opponent’s strategies when designing our maneuver strategy.
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