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Completing Explorer Games with a Deep Reinforcement Learning Framework Based on Behavior Angle Navigation
In cognitive electronic warfare, when a typical combat vehicle, such as an unmanned combat air vehicle (UCAV), uses radar sensors to explore an unknown space, the target-searching fails due to an inefficient servoing/tracking system. Thus, to solve this problem, we developed an autonomous reasoning search method that can generate efficient decision-making actions and guide the UCAV as early as possible to the target area. For high-dimensional continuous action space, the UCAV's maneuveringdoi:10.3390/electronics8050576 fatcat:3lwv5klhefd5hd5kt4n7j7hfkm