Exact Dynamic Programming for Decentralized POMDPs with Lossless Policy 
Compression

Boularias, A; Chaib-draa, B

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Exact Dynamic Programming for Decentralized POMDPs with Lossless Policy Compression

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http://icaps08.cecs.anu.edu.au/
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ICAPS-2008-Boularias.pdf
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Citation

Boularias, A., & Chaib-draa, B. (2008). Exact Dynamic Programming for Decentralized POMDPs with Lossless Policy Compression. In J. Rintanen, B. Nebel, J. Beck, & E. Hansen (Eds.), Eighteenth International Conference on Automated Planning and Scheduling (ICAPS 2008) (pp. 20-27). Menlo Park, CA, USA: AAAI Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C741-8

Abstract

High dimensionality of belief space in DEC-POMDPs is one of the major causes that makes the optimal joint policy computation
intractable. The belief state for a given agent is a
probability distribution over the system states and the policies
of other agents. Belief compression is an efficient POMDP
approach that speeds up planning algorithms by projecting
the belief state space to a low-dimensional one. In this paper,
we introduce a new method for solving DEC-POMDP problems,
based on the compression of the policy belief space.
The reduced policy space contains sequences of actions and
observations that are linearly independent. We tested our approach
on two benchmark problems, and the preliminary results
confirm that Dynamic Programming algorithm scales up
better when the policy belief is compressed.