InforLorV4, Main, Exploration, bibRecord, 001997

Scaling Up Decentralized MDPs Through Heuristic Search

Identifieur interne : 001997 ( Main/Exploration ); précédent : 001996; suivant : 001998

Scaling Up Decentralized MDPs Through Heuristic Search

Auteurs : Jilles Steeve Dibangoye [France] ; Amato Christopher [États-Unis] ; Doniec Arnaud [France]

Source :

RBID : Hal:hal-00765221

Abstract

Decentralized partially observable Markov decision processes (Dec-POMDPs) are rich models for cooperative decision-making under uncertainty, but are often intractable to solve optimally (NEXP-complete). The transition and observation independent Dec-MDP is a general subclass that has been shown to have complexity in NP, but optimal algorithms for this subclass are still inefﬁcient in practice. In this paper, we ﬁrst provide an updated proof that an optimal policy does not depend on the histories of the agents, but only the local observations. We then present a new algorithm based on heuristic search that is able to expand search nodes by using constraint optimization. We show experimental results comparing our approach with the state-of-the-art DecMDP and Dec-POMDP solvers. These results show a reduction in computation time and an increase in scalability by multiple orders of magnitude in a number of benchmarks.

Url:

https://hal.inria.fr/hal-00765221

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 004362
to stream Hal, to step Curation: 004362
to stream Hal, to step Checkpoint: 001613
to stream Main, to step Merge: 001A26
to stream Main, to step Curation: 001997

Le document en format XML

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<front><div type="abstract" xml:lang="en">Decentralized partially observable Markov decision processes (Dec-POMDPs) are rich models for cooperative decision-making under uncertainty, but are often intractable to solve optimally (NEXP-complete). The transition and observation independent Dec-MDP is a general subclass that has been shown to have complexity in NP, but optimal algorithms for this subclass are still inefﬁcient in practice. In this paper, we ﬁrst provide an updated proof that an optimal policy does not depend on the histories of the agents, but only the local observations. We then present a new algorithm based on heuristic search that is able to expand search nodes by using constraint optimization. We show experimental results comparing our approach with the state-of-the-art DecMDP and Dec-POMDP solvers. These results show a reduction in computation time and an increase in scalability by multiple orders of magnitude in a number of benchmarks.</div>
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   |wiki=    Wicri/Lorraine
   |area=    InforLorV4
   |flux=    Main
   |étape=   Exploration
   |type=    RBID
   |clé=     Hal:hal-00765221
   |texte=   Scaling Up Decentralized MDPs Through Heuristic Search
}}

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Scaling Up Decentralized MDPs Through Heuristic Search

Scaling Up Decentralized MDPs Through Heuristic Search

Source :

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

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