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题名: Nonlinear inverse reinforcement learning with mutual information and guassian process
作者: 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: 2014 International Conference on Robotics and Biomimetics (ROBIO 2014)
会议录出版者: IEEE
会议录出版地: Piscataway, NJ, USA
出版日期: 2014
页码: 1445-1450
收录类别: EI
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
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/15412
Appears in Collections:机器人学研究室_会议论文

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