Partitioned Linear Programming Approximations for MDPs

author: Branislav Kveton, Adobe Systems Incorporated
published: July 30, 2008,   recorded: July 2008,   views: 3082

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Description

Approximate linear programming (ALP) is an efficient approach to solving large factored Markov decision processes (MDPs). The main idea of the method is to approximate the optimal value function by a set of basis functions and optimize their weights by linear programming (LP). This paper proposes a new ALP approximation. Comparing to the standard ALP formulation, we decompose the constraint space into a set of low-dimensional spaces. This structure allows for solving the new LP efficiently. In particular, the constraints of the LP can be satisfied in a compact form without an exponential dependence on the tree width of ALP constraints. We study both practical and theoretical aspects of the proposed approach. Moreover, we demonstrate its scale-up potential on an MDP with more than 2100 states.

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Download slides icon Download slides: uai08_kveton_plpa.pdf (708.9 KB)

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