Asynchronous Stochastic Approximation with Differential Inclusions

Details Asynchronous Stochastic Approximation with Differential Inclusions Asynchronous Stochastic Approximation with Differential Inclusions

2011-12-10

arxiv.org/abs/1112.2288v1

Statistics

Machine Learning

41 pages

Scientific paper

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.

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