The multi-agent system driven by the interaction model of fish schooling can emerge excellent characteristics of collective motion, such as scalable group size, local communication of individuals and self-organized collective motion. However, due to the individual differences between robots and real fish, it is difficult for the control model trained by the data of fish schooling to be directly applied to the actual robotics system. Hence, a transfer control method combined with deep learning and deep reinforcement learning is proposed. Firstly, a Deep Neural Network (DNN) model is trained by the data of fish schooling, which is the basement for the interactive control of robots. Then, a deep reinforcement learning method (Deep Deterministic Policy Gradient, DDPG) is connected to the output of DNN model for ensuring the ability of a robot following the wall and moving safely in the group. Finally, based on the above DNN+DDPG model, a key neighbor selection method of the maximum group visual size is designed to expand DNN+DDPG model to multi-agent motion control. Collective motion experiments show that the proposed method can formulate reliable and stable collective motion of the robots via individual information. Compared with the pure DNN transfer control, the proposed DNN+DDPG control frame not only preserves the flexibility of the collective motion of fish schooling, but also enhances the safety and controllability of the robotics system. Thus, there exists strong potential application of the proposed method for the swarm robotics motion control.