Intelligent compression method for underwater acoustic sensor network based on reinforcement learning

Abstract

The application discloses a kind of underwater acoustic sensor network intelligent compression methods based on reinforcement learning, comprising: initialization experience replay buffer D, the network parameter of reinforcement learning intelligent agent;Node carries out real-time perception and feature extraction of local transmission state, obtains the state vector of current time, inputs it into the intelligent agent trained, and the original data packet is compressed with the corresponding compression ratio after optimal compression action of current intelligent decision, and is sent to the next hop node through underwater acoustic channel;After completion, the node obtains environmental feedback information, and the state vector of next time is generated according to the calculation of instant reward value;The transition sample of this interaction is stored in D, and is randomly sampled from D, to minimize loss function as the goal training intelligent agent, until loss function converges.The application can dynamically select optimal compression ratio according to local state, improve data packet delivery rate, reduce transmission delay and energy consumption simultaneously.

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