Asynchronous stochastic approximation with differential inclusions
Asynchronous stochastic approximation with differential inclusions
复制标题
具有微分包含的异步随机近似
DOI：
10.1214/11-ssy056
复制
发表时间：
2011
影响因子：
1.3
通讯作者：
D. Leslie
中科院分区：
文献类型：
--
作者：
Steven Perkins;D. Leslie
本文将Benaim，Hofbauer和Sorin的渐近伪轨线随机逼近方法推广到集值平均场的异步随机逼近。该过程的收敛性被纳入平均场，以产生与等效同步过程相似的收敛结果。此外，这使得许多限制性的假设，以前与异步随机近似被删除。该框架被扩展到一个耦合的异步随机逼近过程与集值平均场。两个时间尺度的论点在这里使用类似的方式在这方面的原始工作博卡。这种方法的适用性证明通过学习在马尔可夫决策过程。
The asymptotic pseudo-trajectory approach to stochastic approximation of Benaim, Hofbauer and Sorin is extended for asynchronous stochastic approximations with a set-valued mean field. The asynchronicity of the process is incorporated into the mean field to produce convergence results which remain similar to those of an equivalent synchronous process. In addition, this allows many of the restrictive assumptions previously associated with asynchronous stochastic approximation to be removed. The framework is extended for a coupled asynchronous stochastic approximation process with set-valued mean fields. Two-timescales arguments are used here in a similar manner to the original work in this area by Borkar. The applicability of this approach is demonstrated through learning in a Markov decision process.