A deep transfer‐learning‐based dynamic reinforcement learning for intelligent tightening system

Reinforcement learning (RL) has been widely applied in the static environment with standard reward functions. For intelligent tightening tasks, it is a challenge to transform expert knowledge into a recognizable mathematical expression for RL agents. Changing assembly standards make the model repeat learning updated knowledge with a high time‐cost. In addition, as the difficulty and low accuracy of designing reward functions, the RL model itself also limits its application in the complex and dynamic engineering environment. To solve the above problems, a deep transfer‐learning‐based dynamic reinforcement learning (DRL‐DTL) is presented and applied in the intelligent tightening system. Specifically, a deep convolution transfer‐learning model (DCTL) is presented to build a mathematical mapping between agents of the model and subjective knowledge, which endows agents to learn from human knowledge efficiently. Then, a dynamic expert library is established to improve the adaptability of algorithm to the changing environment. And an inverse RL based on prior knowledge is presented to acquire reward functions. Experiments are conducted on a tightening assembly system and the results show that the tightening robot with the proposed model can inspect quality problems during the tightening process autonomously and make an adjustment decision based on the optimal policy that the agent calculates.

A deep transfer‐learning‐based dynamic reinforcement learning (DRL‐DTL) model is proposed to enable machine systems in industry to obtain the intelligent decision‐making ability. Data generated in the physical layer (multisensors included) are input to the digital twin layer (DRL‐DTL model included) for feature analysis and optimal solution exploration. The optimal scenario is obtained in the decision layer and is finally fed back to the physical layer. The state space and action space of tightening curves.