Skill enhancement learning with knowledge distillation  

作　　者：Naijun LIU Fuchun SUN Bin FANG Huaping LIU 

机构地区：[1]Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China

出　　处：《Science China(Information Sciences)》2024年第8期202-216,共15页中国科学（信息科学）（英文版）

基　　金：supported by“New Generation Artificial Intelligence”Key Field Research and Development Plan of Guangdong Province(Grant No.2021B0101410002);National Science and Technology Major Project of the Ministry of Science and Technology of China(Grant No.2018AAA0102900);National Natural Science Foundation of China(Grant Nos.U22A2057,62133013).

摘　　要：Skill learning through reinforcement learning has significantly progressed in recent years.How-ever,it often struggles to efficiently find optimal or near-optimal policies due to the inherent trial-and-error exploration in reinforcement learning.Although algorithms have been proposed to enhance skill learning efficacy,there is still much room for improvement in terms of skill learning performance and training sta-bility.In this paper,we propose an algorithm called skill enhancement learning with knowledge distillation(SELKD),which integrates multiple actors and multiple critics for skill learning.SELKD employs knowledge distillation to establish a mutual learning mechanism among actors.To mitigate critic overestimation bias,we introduce a novel target value calculation method.We also perform theoretical analysis to ensure the convergence of SELKD.Finally,experiments are conducted on several continuous control tasks,illustrating the effectiveness of the proposed algorithm.

关 键 词：skill learning enhancement learning reinforcement learning knowledge distillation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]