A multi-UAV task decision-making method based on maddpg
A multi-UAV and decision-making method technology, applied in the field of flight control, can solve the problems of increased environmental complexity, unstable environment, and increased variance, and achieve the effect of increasing the level of intelligence, improving combat capabilities, and ensuring survivability
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[0149] In this example, the final network structure is designed as: Actor network structure is fully connected neural network [56; 56; 2], Critic network structure is fully connected neural network [118; 78; 36; 1], two neural networks The hidden layers all use the RELU function as the activation function, such as Image 6 shown. During training, the mini-batch size is 1024, the maximum learning step size (maxepisode) is 30000, the update rate of the auxiliary network is τ=0.01, the learning rate of the critical network is 0.01, and the learning rate of the actor network is 0.001. The AdamOptimizer optimizer is used for learning. The size of the experience pool is 1,000,000. Once the data in the experience pool exceeds the maximum value, the original experience data will be lost, and the constructed multi-UAV task decision-making network will achieve optimal performance.
[0150] The invention initializes the positions of three unmanned aerial vehicles in a designated area in...
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