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Error-Bounded Approximations for Infinite-Horizon Discounted Decentralized POMDPs

Jilles Steeve Dibangoye 1 Olivier Buffet 1 François Charpillet 1 
1 MAIA - Autonomous intelligent machine
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : We address decentralized stochastic control problems represented as decentralized partially observable Markov decision processes (Dec-POMDPs). This formalism provides a general model for decision-making under uncertainty in cooperative, decentralized settings, but the worst-case complexity makes it difficult to solve optimally (NEXP-complete). Recent advances suggest recasting Dec-POMDPs into continuous-state and deterministic MDPs. In this form, however, states and actions are embedded into high-dimensional spaces, making accurate estimate of states and greedy selection of actions intractable for all but trivial-sized problems. The primary contribution of this paper is the first framework for error-monitoring during approximate estimation of states and selection of actions. Such a framework permits us to convert state-of-the-art exact methods into error-bounded algorithms, which results in a scalability increase as demonstrated by experiments over problems of unprecedented sizes.
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Submitted on : Wednesday, December 17, 2014 - 5:32:39 PM
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Jilles Steeve Dibangoye, Olivier Buffet, François Charpillet. Error-Bounded Approximations for Infinite-Horizon Discounted Decentralized POMDPs. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Sep 2014, Nancy, France. pp.338 - 353, ⟨10.1007/978-3-662-44848-9_22⟩. ⟨hal-01096610⟩



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