Mean-variance optimization of discrete time discounted Markov decision processes
Mean-variance optimization of discrete time discounted Markov decision processes
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离散时间贴现马尔可夫决策过程的均值-方差优化
DOI:
10.1016/j.automatica.2017.11.012
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发表时间:
2017-08
期刊:
影响因子:
6.4
通讯作者:
Li Xia
中科院分区:
文献类型:
--
作者:
Li Xia
In this paper, we study a mean–variance optimization problem in an infinite horizon discrete time discounted Markov decision process (MDP). The objective is to minimize the variance of system rewards with the constraint of mean performance. Different from most of works in the literature which require the mean performance already achieve optimum, we can let the discounted performance equal any constant. The difficulty of this problem is caused by the quadratic form of the variance function which makes the variance minimization problem not a standard MDP. By proving the decomposable structure of the feasible policy space, we transform this constrained variance minimization problem to an equivalent unconstrained MDP under a new discounted criterion and a new reward function. The difference of the variances of Markov chains under any two feasible policies is quantified by a difference formula. Based on the variance difference formula, a policy iteration algorithm is developed to find the optimal policy. We also prove the optimality of deterministic policy over the randomized policy generated in the mean-constrained policy space. Numerical experiments demonstrate the effectiveness of our approach.
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DOI:
--
发表时间:
2012-11
期刊:
2012 2nd Australian Control Conference
影响因子:
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DOI:
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发表时间:
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期刊:
Discrete Event Dyn. Syst.
影响因子:
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