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In this article, the underwater target tracking control problem of a biomimetic underwater vehicle (BUV) is addressed. Since it is difficult to build an effective mathematic model of a BUV due to the uncertainty of hydrodynamics, target tracking control is converted into the Markov decision process and is further achieved via deep reinforcement learning. The system state and reward function of underwater target tracking control are described. Based on the actor–critic reinforcement learning framework, the deep deterministic policy gradient actor–critic algorithm with supervision controller is proposed. The training tricks, including prioritized experience replay, actor network indirect supervision training, target network updating with different periods, and expansion of exploration space by applying random noise, are presented. Indirect supervision training is designed to address the issues of low stability and slow convergence of reinforcement learning in the continuous state and action space. Comparative simulations are performed to show the effectiveness of the training tricks. Finally, the proposed actor–critic reinforcement learning algorithm with supervision controller is applied to the physical BUV. Swimming pool experiments of underwater object tracking of the BUV are conducted in multiple scenarios to verify the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Few-shot learning, aiming to learn novel concepts from one or a few labeled examples, is an interesting and very challenging problem with many practical advantages. Existing few-shot methods usually utilize data of the same classes to train the feature embedding module and in a row, which is unable to learn adapting to new tasks. Besides, traditional few-shot models fail to take advantage of the valuable relations of the support-query pairs, leading to performance degradation. In this article, we propose a transductive relation-propagation graph neural network (GNN) with a decoupling training strategy (TRPN-D) to explicitly model and propagate such relations across support-query pairs, and empower the few-shot module the ability of transferring past knowledge to new tasks via the decoupling training. Our few-shot module, namely TRPN, treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intraclass commonality and interclass uniqueness. Through relation propagation, the model could generate the discriminative relation embeddings for support-query pairs. To the best of our knowledge, this is the first work that decouples the training of the embedding network and the few-shot graph module with different tasks, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods. [ABSTRACT FROM AUTHOR]