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Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning

机译：基于深度加强学习的机动决策无人机

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摘要

With the development of artificial intelligence and integrated sensor technologies, unmanned aerial vehicles (UAVs) are more and more applied in the air combats. A bottleneck that constrains the capability of UAVs against manned vehicles is the autonomous maneuver decision, which is a very challenging problem in the short-range air combat undergoing highly dynamic and uncertain maneuvers of enemies. In this paper, an autonomous maneuver decision model is proposed for the UAV short-range air combat based on reinforcement learning, which mainly includes the aircraft motion model, one-to-one short-range air combat evaluation model and the maneuver decision model based on deep Q network (DQN). However, such model includes a high dimensional state and action space which requires huge computation load for DQN training using traditional methods. Then, a phased training method, called "basic-confrontation", which is based on the idea that human beings gradually learn from simple to complex is proposed to help reduce the training time while getting suboptimal but efficient results. Finally, one-to-one short-range air combats are simulated under different target maneuver policies. Simulation results show that the proposed maneuver decision model and training method can help the UAV achieve autonomous decision in the air combats and obtain an effective decision policy to defeat the opponent.
机译：随着人工智能和集成传感器技术的发展，无人驾驶飞行器（无人机）越来越多地应用于空战。一种限制无人机对载人车辆的能力的瓶颈是自主机动决策，这是一个非常具有挑战性的敌人，不确定的敌人的机动。在本文中，提出了一种基于加强学习的无人机短距空战，主要包括飞机运动模型，一对一的短程空战评估模型和基于机动决策模型的自动运转决策模型深度Q网络（DQN）。然而，这种模型包括高维状态和动作空间，其需要使用传统方法进行DQN训练的巨大计算负载。然后，提出了一种被称为“基本对抗”的分阶段培训方法，这是基于人类逐渐被学习从简单到复杂的想法，以帮助减少次优，但有效的培训时间。最后，在不同的目标机动策略下模拟了一对一的短距离空调。仿真结果表明，拟议的机动决策模型和培训方法可以帮助无人机在空战中实现自主决策，并获得击败对手的有效决策政策。

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