A deep transfer‐learning‐based dynamic reinforcement learning for intelligent tightening system,International Journal of Intelligent Systems

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A deep transfer‐learning‐based dynamic reinforcement learning for intelligent tightening system
International Journal of Intelligent Systems ( IF 5.0 ) Pub Date : 2020-12-30 , DOI: 10.1002/int.22345
Wentao Luo ₁ , Jianfu Zhang _{1,

2} , Pingfa Feng _{1,

2,

3} , Dingwen Yu ₁ , Zhijun Wu ₁

Affiliation

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.

中文翻译：

基于深度学习的动态强化学习，用于智能拧紧系统

具有标准奖励功能的强化学习（RL）已广泛应用于静态环境。对于智能拧紧任务，将专家知识转换为RL代理可识别的数学表达式是一个挑战。不断变化的装配标准使该模型花费大量时间重复学习更新的知识。此外，由于设计奖励函数的难度和准确性较低，RL模型本身也限制了其在复杂而动态的工程环境中的应用。为了解决上述问题，提出了一种基于深度迁移学习的动态强化学习（DRL-DTL），并将其应用于智能拧紧系统。具体来说，我们提出了深度卷积转移学习模型（DCTL），以在模型的主体和主观知识之间建立数学映射，使代理商能够有效地从人类知识中学习。然后，建立动态专家库，以提高算法对变化环境的适应性。提出了一种基于先验知识的逆RL算法，以获取奖励函数。在拧紧装配系统上进行了实验，结果表明，提出的模型的拧紧机器人可以自动检查拧紧过程中的质量问题，并根据代理商计算出的最佳策略做出调整决策。

更新日期：2021-01-29

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