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Convergence analysis of an incremental approach to online inverse reinforcement learning

Journal of Zhejiang University-Science C(Computers and Electronics) 2011年第1期12卷 17-24页

作者： Zhuo-jun JIN Hui QIAN Shen-yi CHEN Miao-liang ZHU School of Computer Science and Technology Zhejiang University Hangzhou 310027 China

Interest in inverse reinforcement learning (IRL) has recently increased,that is,interest in the problem of recovering the reward function underlying a Markov decision process (MDP) given the dynamics of the system and the behavior of an *** paper deals with an incremental approach to online ***,the convergence property of the incremental method for the IRL problem was investigated,and the bounds of both the mistake number during the learning process and regret were provided by using a detailed *** an online algorithm based on incremental error correcting was derived to deal with the IRL *** key idea is to add an increment to the current reward estimate each time an action mismatch *** leads to an estimate that approaches a target optimal *** proposed method was tested in a driving simulation experiment and found to be able to efficiently recover an adequate reward function.

关键词： Incremental approach Reward recovering Online learning inverse reinforcement learning Markov decision process

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A survey of inverse reinforcement learning techniques

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International Journal of Intelligent Computing and Cybernetics 2012年第3期5卷 293-311页

作者： Shao Zhifei Er Meng Joo School of Electrical and Electronics Engineering Nanyang Technological UniversitySingapore

Purpose-This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning(IRL).Design/methodology/approach-reinforcement learning(RL)techniques provide a powerful solution for sequential decision making problems under *** uses an agent equipped with a reward function to find a policy through interactions with a dynamic ***,one major assumption of existing RL algorithms is that reward function,the most succinct representation of the designer’s intention,needs to be provided *** practice,the reward function can be very hard to specify and exhaustive to tune for large and complex problems,and this inspires the development of IRL,an extension of RL,which directly tackles this problem by learning the reward function through expert *** this paper,the original IRL algorithms and its close variants,as well as their recent advances are reviewed and ***-This paper can serve as an introduction guide of fundamental theory and developments,as well as the applications of ***/value-This paper surveys the theories and applications of IRL,which is the latest development of RL and has not been done so far.

关键词： inverse reinforcement learning Reward function reinforcement learning Artificial intelligence learning methods

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Heterogeneous multi-player imitation learning

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Control Theory and Technology 2023年第3期21卷 281-291页

作者： Bosen Lian Wenqian Xue Frank L.Lewis Department of Electrical and Computer Engineering Auburn UniversityAuburnAL 36849USA State Key Laboratory of Synthetical Automation for Process Industries and International Joint Research Laboratory of Integrated Automation Northeastern UniversityShenyang 110819LiaoningChina UTA Research Institute University of Texas at Arlington(UTA)Fort WorthTX 76118USA

This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input *** propose a model-free data-driven inverse reinforcement learning(RL)algorithm for a leaner to find the cost functions of a N-player Nash expert system given the expert's states and control *** allows us to address the imitation learning problem without prior knowledge of the expert's system *** achieve this,we provide a basic model-based algorithm that is built upon RL and inverse optimal *** serves as the foundation for our final model-free inverse RL algorithm which is implemented via neural network-based value function *** analysis and simulation examples verify the methods.

关键词： Imitation learning inverse reinforcement learning Heterogeneous multi-player games Data-driven model-free control

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Robot learning from demonstration for path planning: A review

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Science China(Technological Sciences) 2020年第8期63卷 1325-1334页

作者： XIE ZongWu ZHANG Qi JIANG ZaiNan LIU Hong State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

learning from demonstration(LfD)is an appealing method of helping robots learn new *** papers have presented methods of LfD with good performance in ***,complicated robot tasks that need to carefully regulate path planning strategies remain *** or non-contact constraints in specific robot tasks make the path planning problem more difficult,as the interaction between the robot and the environment is *** this paper,we focus on the path planning of complex robot tasks in the domain of LfD and give a novel perspective for classifying imitation learning and inverse reinforcement *** classification is based on constraints and obstacle ***,we summarize these methods and present promising directions for robot application and LfD theory.

关键词： learning from demonstration path planning imitation learning inverse reinforcement learning obstacle avoidance

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