Optimality of LSTD and its Relation to MC

机译：LSTD的最优性及其与MC的关系

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In this analytical study we compare the risk of the Monte Carlo (MC) and the least-squares TD (LSTD) estimator. We prove that for the case of acyclic Markov Reward Processes (MRPs) LSTD has minimal risk for any convex loss function in the class of unbiase

机译：在本分析研究中，我们比较了蒙特卡洛（MC）和最小二乘TD（LSTD）估计量的风险。我们证明，对于非循环马尔可夫奖励过程（MRP），LSTD对于不平衡类中的任何凸损失函数具有最小的风险

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《Neural Networks, The 2007 IEEE International Joint Conference on》|2007年|P.338-343|共6页
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Grunewalder; S.; Hochreiter; S.; Obermayer; K.;
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中图分类工业技术;
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Markov processes; Monte Carlo methods; convex programming; estimation theory; least squares approximations; statistical analysis; Monte Carlo method; acyclic Markov reward process; convex loss function; least-squares temporal difference estimator; statistical esti;

机译：马尔可夫过程;蒙特卡罗方法;凸规划;估计理论;最小二乘逼近;统计分析;蒙特卡罗方法;无环马尔可夫奖励过程;凸损失函数;最小二乘时差估计器;统计估计;

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Optimality of LSTD and its Relation to MC

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