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Scalable Planning and Learning for Multiagent POMDPs

Christopher Amato and Frans A. Oliehoek. Scalable Planning and Learning for Multiagent POMDPs. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 1995–2002, January 2015.
[Please see the extended version for pseudo code and proofs.]

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Abstract

Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces. This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents. To combat this intractability, we propose a novel scalable approach based on sample-based planning and factored value functions that exploits structure present in many multiagent settings. This approach applies not only in the planning case, but also the Bayesian reinforcement learning setting. Experimental results show that we are able to provide high quality solutions to large multiagent planning and learning problems.

BibTeX Entry

@inproceedings{Amato15AAAI,
    author =        {Christopher Amato and Frans A. Oliehoek},
    title =         {Scalable Planning and Learning for Multiagent {POMDPs}},
    booktitle =     AAAI15,
    month =         jan,
    year =          2015,
    pages =         {1995--2002},
    wwwnote =       {[Please see the <a href="b2hd-Amato15AAAI_extended.html">extended version</a> for pseudo code and proofs.]},
    abstract = {
    Online, sample-based planning algorithms for POMDPs have shown great promise 
    in scaling to problems with large state spaces, but they become intractable 
    for large action and observation spaces.  This is particularly problematic 
    in multiagent POMDPs where the action and observation space grows exponentially 
    with the number of agents.  To combat this intractability, we propose a novel 
    scalable approach based on sample-based planning and factored value functions 
    that exploits structure present in many multiagent settings.  This approach 
    applies not only in the planning case, but also the Bayesian reinforcement 
    learning setting.  Experimental results show that we are able to provide high 
    quality solutions to large multiagent planning and learning problems. 
    }
}

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