Quasi stochastic approximation
Quasi stochastic approximation
复制标题
拟随机近似
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Sean P. Meyn
中科院分区：
文献类型：
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作者：
Darshan Shirodkar;Sean P. Meyn
最近的工作表明，可以制定随机近似的确定性模拟来获得用于确定性和随机系统的近似最优控制的 Q 学习算法。本文为“准随机近似”提供了一般基础，其中所考虑的所有过程都是确定性的，就像模拟中方差减少的准蒙特卡罗一样。描述了求根和 TD 学习的应用，并给出了数值结果。
In recent work it was shown that a deterministic analog of stochastic approximation can be formulated to obtain a Q-learning algorithm for approximate optimal control of deterministic and stochastic systems. This paper provides a general foundation for "quasi-stochastic approximation" in which all of the processes under consideration are deterministic, much like quasi-Monte-Carlo for variance reduction in simulation. Applications to root finding and to TD-learning are described, and numerical results are presented.