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基于学习的线性时序逻辑规范马尔可夫决策过程控制合成方法

A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications

作者:Dorsa Sadigh;Eric Kim;Samuel Coogan;S.Shankar Sastry;Sanjit A.Seshia 作者单位:EECS Department, University of California, Berkeley 加工时间:2015-03-29 信息来源:EECS 索取原文[10 页]
关键词:线性时序逻辑;马尔可夫决策过程;控制合成
摘 要:We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property.We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically with a case study in traffic control that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one.
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