Model-free reinforcement learning from expert demonstrations: a survey

被引:0
作者
Jorge Ramírez
Wen Yu
Adolfo Perrusquía
机构
[1] CINVESTAV-IPN (National Polytechnic Institute),Departamento de Control Automático
[2] Cranfield University,School of Aerospace, Transport and Manufacturing
来源
Artificial Intelligence Review | 2022年 / 55卷
关键词
Reinforcement learning; Imitation learning; Learning from demonstrations; Behavioral learning; Demonstrations;
D O I
暂无
中图分类号
学科分类号
摘要
Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation learning with reinforcement learning that seeks to take advantage of these two learning approaches. RLED uses demonstration trajectories to improve sample efficiency in high-dimensional spaces. RLED is a new promising approach to behavioral learning through demonstrations from an expert teacher. RLED considers two possible knowledge sources to guide the reinforcement learning process: prior knowledge and online knowledge. This survey focuses on novel methods for model-free reinforcement learning guided through demonstrations, commonly but not necessarily provided by humans. The methods are analyzed and classified according to the impact of the demonstrations. Challenges, applications, and promising approaches to improve the discussed methods are also discussed.
引用
收藏
页码:3213 / 3241
页数:28
相关论文
共 104 条
[11]  
Taylor ME(2013)Reinforcement learning in robotics: a survey Int J Robot Res 32 1238-148
[12]  
Bellman R(2011)Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input Adv Robot 25 581-612
[13]  
Bengio Y(2013)Reinforcement learning in robotics: applications and real-world challenges Robotics 2 122-105
[14]  
Courville A(2016)Analysis of classification-based policy iteration algorithms J Mach Learn Res 17 583-841
[15]  
Vincent P(2012)Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers IEEE Control Syst Mag 32 76-10
[16]  
Chen SA(2019)Introspective Q-learning and learning from demonstration Knowl Eng Rev 34 e8-533
[17]  
Tangkaratt V(1983)Robot programming Proc IEEE 71 821-2030
[18]  
Lin HT(2020)The mineRL competition on sample-efficient reinforcement learning using human priors: a retrospective J Mach Learn Res 1 1-489
[19]  
Sugiyama M(2015)Human-level control through deep reinforcement learning Nature 518 529-22
[20]  
Ecoffet A(2020)A framework for learning from demonstration with minimal human effort IEEE Robot Autom Lett 5 2023-354