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Nonlinear inverse reinforcement learning with mutual information and guassian process
Li DC(李德才); He YQ(何玉庆); Gu F(谷丰)
作者部门机器人学研究室
会议名称2014 International Conference on Robotics and Biomimetics (ROBIO 2014)
会议日期December 5-10, 2014
会议地点Bali, Indonesia
会议录名称2014 International Conference on Robotics and Biomimetics (ROBIO 2014)
出版者IEEE
出版地Piscataway, NJ, USA
2014
页码1445-1450
收录类别EI ; CPCI(ISTP)
EI收录号20152100876978
WOS记录号WOS:000380399500240
产权排序1
ISBN号978-1-4799-7396-5
摘要In this paper, a mutual information (MI) and Extreme Learning Machine (ELM) based inverse reinforcement learning (IRL) algorithm, which termed as MEIRL, is proposed to construct nonlinear reward function. The basic idea of MIIRL is that, similar to GPIRL, the reward function is learned by using Gaussian process and the importance of each feature is obtained by using automatic relevance determination (ARD). Then mutual information is employed to evaluate the impact of each feature to the reward function, based on which extreme learning machine is introduced along with an adaptive model construction procedure to choose the optimal subset of features and the performance of the original GPIRL algorithm is enhanced as well. Furthermore, to demonstrate the effectiveness of MEIRL, a simulation called highway driving is constructed. The simulation results show that MEIRL is comparable with the state of art IRL algorithms in terms of generalization capability, but more efficient while the number of features is large.
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/15412
专题机器人学研究室
通讯作者Li DC(李德才)
作者单位Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
推荐引用方式
GB/T 7714
Li DC,He YQ,Gu F. Nonlinear inverse reinforcement learning with mutual information and guassian process[C]. Piscataway, NJ, USA:IEEE,2014:1445-1450.
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