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arXiv · 1704.02544

A Linearly Relaxed Approximate Linear Program for Markov Decision Processes

Chandrashekar Lakshminarayanan·Shalabh Bhatnagar·Csaba Szepesvari
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Abstract

Approximate linear programming (ALP) and its variants have been widely applied to Markov Decision Processes (MDPs) with a large number of states. A serious limitation of ALP is that it has an intractable number of constraints, as a result of which constraint approximations are of interest. In this paper, we define a linearly relaxed approximation linear program (LRALP) that has a tractable number of constraints, obtained as positive linear combinations of the original constraints of the ALP. The main contribution is a novel performance bound for LRALP.

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Chandrashekar Lakshminarayanan, Shalabh Bhatnagar, Csaba Szepesvari. 2017-04-09. A Linearly Relaxed Approximate Linear Program for Markov Decision Processes. https://arxiv.org/abs/1704.02544

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