Central South University | through exploration and understanding: find interpretable features with deep reinforcement learning

【 title 】Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning

【 The author team 】Jiawen Wei, Zhifeng Qiu, Fangyuan Wang, Wenwei Lin, Ning Gui, Weihua Gui

【 Date of publication 】2022.6.28

【 Thesis link 】https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9810174

【 Recommended reasons 】 Understanding the environment through interaction has become one of the most important intellectual activities for human beings to master unknown systems . as everyone knows , Deep reinforcement learning (DRL) In many applications, effective control is achieved through human like exploration and utilization . However , Deep neural network (DNN) The opacity of often hides the key information related to control , This is essential for understanding the target system . This paper first proposes a new online feature selection framework , That is, attention feature selection based on two worlds (D-AFS) , To identify the contribution of input to the whole control process . With most DRL The world used in is different ,D-AFS It has both the real world and the distorted virtual world . Newly introduced attention based assessment (AR) The module realizes the dynamic mapping from the real world to the virtual world . The existing DRL The algorithm needs only a little modification , You can learn in the dual world . Through analysis DRL Response in two worlds ,D-AFS It can quantitatively identify the importance of each feature to control .