The aggregation task is one of the typical tasks of Unmanned Aerial Vehicle(UAV) operations and has always been a hot research topic. With the development of technology and the continuous proves of war, multi-UAV have...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
The aggregation task is one of the typical tasks of Unmanned Aerial Vehicle(UAV) operations and has always been a hot research topic. With the development of technology and the continuous proves of war, multi-UAV have significant advantages in combat compared to single UAV. To address the intelligent control problem of multi-UAV aggregation task, this paper proposes a Twin Delayed Deep Deterministic policy gradient(td3) algorithm based on an evolutionary algorithm and introduces an evolutionary reinforcement learning framework. A learning cross operator is proposed to make offspring inherit parent features through training. To avoid high interaction costs, a critic network-assisted evaluation mechanism is proposed,and a response control decision is designed to combine real and virtual fitness to improve accuracy. Therefore, a Twin Delayed Deep Deterministic policy gradient algorithm combining Genetic algorithm enhanced by learning cross factors and auxiliary evaluation(ga-td3) is proposed. Simulation results show that ga-td3 has better training effects and generalization.
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