Advanced Search
MyIDEAS: Login

Learning From Actions Not Taken In Multiagent Systems

Contents:

Author Info

  • KAGAN TUMER

    ()
    (Oregon State University, 204 Rogers Hall, Corvallis, Oregon 97331, USA)

  • NEWSHA KHANI

    ()
    (Oregon State University, 204 Rogers Hall, Corvallis, Oregon 97331, USA)

Registered author(s):

    Abstract

    In large cooperative multiagent systems, coordinating the actions of the agents is critical to the overall system achieving its intended goal. Even when the agents aim to cooperate, ensuring that the agent actions lead to good system level behavior becomes increasingly difficult as systems become larger. One of the fundamental difficulties in such multiagent systems is the slow learning process where an agent not only needs to learn how to behave in a complex environment, but also needs to account for the actions of other learning agents. In this paper, we present a multiagent learning approach that significantly improves the learning speed in multiagent systems by allowing an agent to update its estimate of the rewards (e.g. value function in reinforcement learning) for all its available actions, not just the action that was taken. This approach is based on an agent estimating the counterfactual reward it would have received had it taken a particular action. Our results show that the rewards on such "actions not taken" are beneficial early in training, particularly when only particular "key" actions are used. We then present results where agent teams are leveraged to estimate those rewards. Finally, we show that the improved learning speed is critical in dynamic environments where fast learning is critical to tracking the underlying processes.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.worldscinet.com/cgi-bin/details.cgi?type=pdf&id=pii:S0219525909002301
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: http://www.worldscinet.com/cgi-bin/details.cgi?type=html&id=pii:S0219525909002301
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal Advances in Complex Systems.

    Volume (Year): 12 (2009)
    Issue (Month): 04 ()
    Pages: 455-473

    as in new window
    Handle: RePEc:wsi:acsxxx:v:12:y:2009:i:04:p:455-473

    Contact details of provider:
    Web page: http://www.worldscinet.com/acs/acs.shtml

    Order Information:
    Email:

    Related research

    Keywords: Multiagent learning; counterfactual reward; difference reward;

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:wsi:acsxxx:v:12:y:2009:i:04:p:455-473. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tai Tone Lim).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.