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Reports (Technical Report) Year : 2008

Using linear programming duality for solving finite horizon Dec-POMDPs

Raghav Aras
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Alain Dutech

Abstract

This paper studies the problem of finding an optimal finite horizon joint policy for a decentralized partially observable Markov decision process (Dec-POMDP). We present a new algorithm for finding an optimal joint policy. The algorithm is based on the fact that the necessary condition for a joint policy to be optimal is that it be locally optimal (that is, a Nash equilibrium). Through the application of linear programming duality, the necessary condition can be transformed to a nonlinear program which can then further be transformed to a 0-1 mixed integer linear program (MILP) whose optimal solution is an optimal joint policy (in the sequence form). The proposed algorithm thus consists of solving this 0-1 MILP. Computational experience of the 0-1 MILP on two and three agent DEC-POMDPs gives mixed results. On some problems it is faster than existing algorithms, on others it is slower.
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Dates and versions

inria-00320645 , version 1 (16-09-2008)

Identifiers

  • HAL Id : inria-00320645 , version 1

Cite

Raghav Aras, Alain Dutech, François Charpillet. Using linear programming duality for solving finite horizon Dec-POMDPs. [Technical Report] RR-6641, INRIA. 2008, pp.27. ⟨inria-00320645⟩
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