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Autonomous Decision-Making Method for Combat Mission of UAV based on Deep Reinforcement Learning

机译：基于深度强化学习的无人机作战任务自主决策方法

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Improving of the autonomous decision-making ability of UAV has become a key factor for UAV to seize the initiative of future battlefield. At present, UAV's tasks mainly depend on pre-planning, which is difficult to adapt to the complexity and dynamics of future battlefield. Aiming at this problem, in view of typical mission scenarios of UAV regional reconnaissance and air-to-air confrontation, this paper adopts Deep Learning method to develop autonomous decision-making method for UAV, constructs mission decision-making model of Deep Belief Network (DBN) and Q-Learning algorithm, and then optimizes the decision-making model based on genetic algorithm to realize "off-line learning" and "online decision-making" provide effective support. The simulation results verify that the method can effectively deal with typical tasks of UAV regional reconnaissance and air-to-air confrontation, and it is an effective attempt to carry out autonomous decision-making of UAV.
机译：无人机自主决策能力的提高已成为无人机抢占未来战场主动权的关键因素。目前，无人机的任务主要取决于预先计划，这很难适应未来战场的复杂性和动态。针对这一问题，针对无人机典型区域侦察和空空对抗的任务场景，本文采用深度学习方法开发无人机自主决策方法，构建了深度信念网络任务决策模型（ DBN）和Q-Learning算法，然后基于遗传算法优化决策模型，为“离线学习”和“在线决策”提供有效的支持。仿真结果验证了该方法能够有效处理无人机区域侦察和空空对抗的典型任务，是开展无人机自主决策的有效尝试。

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