When the depth deterministic strategy gradient(DDPG)algorithm is applied to the motion control of unmanned cabled remote-controled underwater robot,several new problems such as the bad samples affect the learning stability,lack the ability to explore the environment are happened,and the learning time is difficult to cover the teaching of the algorithm.Hence,the DDPG algorithm is improved from three aspects:neural network structure,noise introduction and fusion supervised learning,and a supervised DDPG control algorithm based on hybrid neural network structure and parameter noise is proposed.The simulation results show that the improved DDPG algorithm is more effective than the conventional DDPG algorithm and the traditional PID algorithm.
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