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2016

Optimally Solving Dec-POMDPs as Continuous-State MDPs

3 years 8 months ago
Optimally Solving Dec-POMDPs as Continuous-State MDPs
Optimally solving decentralized partially observable Markov decision processes (Dec-POMDPs) is a hard combinatorial problem. Current algorithms search through the space of full histories for each agent. Because of the doubly exponential growth in the number of policies in this space as the planning horizon increases, these methods quickly become intractable. However, in real world problems, computing policies over the full history space is often unnecessary. True histories experienced by the agents often lie near a structured, low-dimensional manifold embedded into the history space. We show that by transforming a Dec-POMDP into a continuous-state MDP, we are able to find and exploit these low-dimensional representations. Using this novel transformation, we can then apply powerful techniques for solving POMDPs and continuous-state MDPs. By combining a general search algorithm and dimension reduction based on feature selection, we introduce a novel approach to optimally solve problems...
Jilles Steeve Dibangoye, Christopher Amato, Olivie
Added 06 Apr 2016
Updated 06 Apr 2016
Type Journal
Year 2016
Where JAIR
Authors Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, François Charpillet
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