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arXiv · 2102.00030

Optimistic Policy Iteration for MDPs with Acyclic Transient State Structure

Joseph Lubars·Anna Winnicki·Michael Livesay·R. Srikant
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Abstract

We consider Markov Decision Processes (MDPs) in which every stationary policy induces the same graph structure for the underlying Markov chain and further, the graph has the following property: if we replace each recurrent class by a node, then the resulting graph is acyclic. For such MDPs, we prove the convergence of the stochastic dynamics associated with a version of optimistic policy iteration (OPI), suggested in Tsitsiklis (2002), in which the values associated with all the nodes visited during each iteration of the OPI are updated.

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Joseph Lubars, Anna Winnicki, Michael Livesay, R. Srikant. 2021-01-29. Optimistic Policy Iteration for MDPs with Acyclic Transient State Structure. https://arxiv.org/abs/2102.00030

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