Minimizing risk models in stochastic shortest path problems
Minimizing risk models in stochastic shortest path problems
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DOI：
10.1007/s001860200246
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发表时间：
2003-04
影响因子：
1.2
通讯作者：
Yoshio Ohtsubo
中科院分区：
文献类型：
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作者：
Yoshio Ohtsubo
我们考虑一个随机最短路问题中的风险最小化模型，其中对于图中的每个节点，我们在后继节点的集合上选择概率分布，以便以最小阈值概率到达给定的目标节点。我们制定这样一个问题，作为非折扣有限马尔可夫决策过程。我们证明了最优值函数是最优性方程的唯一解，并找到了最优平稳策略。给出了数值迭代方法。
We consider a minimizing risk model in a stochastic shortest path problem in which for each node of a graph we select a probability distribution over the set of successor nodes so as to reach a given target node with minimum threshold probability. We formulate such a problem as undiscounted finite Markov decision processes. We show that an optimal value function is a unique solution to an optimality equation and find an optimal stationary policy. A value iteration method is also given.