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dc.contributor.authorLiu, Yunlongzh_CN
dc.contributor.authorJi, Guolizh_CN
dc.contributor.authorYang, Zijiangzh_CN
dc.contributor.authorFarzindar, Azh_CN
dc.contributor.authorKeselj, Vzh_CN
dc.contributor.author吉国力zh_CN
dc.date.accessioned2013-12-12T02:49:27Z
dc.date.available2013-12-12T02:49:27Z
dc.date.issued2010zh_CN
dc.identifier.citationAdvances in Artificial Intelligence,6085309-314zh_CN
dc.identifier.otherWOS:000281548500032zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/70724
dc.description.abstractAs an alternative to partially observable Markov decision processes (POMDPs), Predictive State Representations (PSRs) is a recently developed method to model controlled dynamical systems. While POMDPs and PSRs provide general frameworks for solving the problem of planning under uncertainty, they rely crucially on having a known and accurate model of the environment. However, in real-world applications it can be extremely difficult to build an accurate model. In this paper, we use learned PSR model for planning under uncertainty, where the PSR model is learned from samples and may be inaccurate. We demonstrate the effectiveness of our algorithm on a standard set of POMDP test problems. Empirical results show that the algorithm we proposed is effective.zh_CN
dc.language.isoen_USzh_CN
dc.subjectPartially observable Markov decision processes, Predictive State Representations, Learned PSR model, Planning under uncertainty.zh_CN
dc.titleUsing Learned PSR Model for Planning under Uncertaintyzh_CN
dc.typeArticlezh_CN


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