Resource-Constrained Sensor Signal Collecting Method Based on Reinforcement Learning Algorithm
For the signal collecting efficiency problem of the resource-constrained sensors under autonomous conditions,the sig-nal collecting model of the resource-constrained sensors was proposed.Through the collecting signal channel selection algorithm based on reinforcement learning is proposed to improve the effective collection efficiency of signal samples.The learning effects of Q-Learn-ing,DQN and DDPG method were also compared in this paper.The experiment results show that the proposed method can provide high efficiency of signal samples collecting.With suitable super parameters,the collecting efficiency can be promoted to the 95%val-ue of the theoretical limits,which proves that the intelligent collection model can effectively improve the intelligent level of sensor au-tonomous collection.