Reinforcement learning adaptive stochastic resonance method for underwater weak signal detection

A reinforcement learning adaptive stochastic resonance method for underwater weak signal detection comprises the following steps: 1, calculating the signal to noise ratio of a signal before the signal enters a stochastic resonance system; 2, coding possible solutions of parameters a and b of the stochastic resonance system, forming a gene space, and segmenting the gene space into n sub-spaces; 3, creating n Agents, taking the n sub-spaces as action spaces of the n Agents, and initializing each Q value; 4, determining a Q-Learning action and obtaining experience knowledge and a training example; 5, calculating the signal to noise ratio every time and using the signal to noise ratio for evaluation of individual fitness and as an environment reward to update the Q value and carrying out elite retention; 6, determining whether a termination condition is satisfied, outputting the parameters a and b of the round as the optimal parameters if the termination condition is satisfied, or repeating from the action select