Skip to Main content Skip to Navigation

Using linear programming duality for solving finite horizon Dec-POMDPs

Raghav Aras 1 Alain Dutech 1 François Charpillet 1 
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Raghav Aras Connect in order to contact the contributor
Submitted on : Tuesday, September 16, 2008 - 2:49:56 PM
Last modification on : Wednesday, February 2, 2022 - 3:51:45 PM
Long-term archiving on: : Tuesday, June 28, 2011 - 4:35:05 PM


Files produced by the author(s)


  • HAL Id : inria-00320645, version 1



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⟩



Record views


Files downloads