Extracting plans from reinforcement learners

forcement learning algorithms that generate only reactive policies and existing probabilistic planning algorithms that requires a substantial amount of a priori knowledge in order to plan we devise a two stage bottom up learning to plan process in which rst reinforcement learn ing dynamic programming is applied without the use of a priori domain speci c knowledge to acquire a reactive policy and then explicit plans are extracted from the learned reactive policy Plan extraction is based on a beam search algorithm that performs temporal projection in a restricted fashion guided by the value functions resulting from reinforcement learn ing dynamic programming..
Keywords No keywords specified (fix it)
Categories No categories specified
(categorize this paper)
 Save to my reading list
Follow the author(s)
My bibliography
Export citation
Find it on Scholar
Edit this record
Mark as duplicate
Revision history Request removal from index Translate to english
Download options
PhilPapers Archive

Upload a copy of this paper     Check publisher's policy on self-archival     Papers currently archived: 9,360
External links
  •   Try with proxy.
  • Through your library Only published papers are available at libraries
    References found in this work BETA

    No references found.

    Citations of this work BETA

    No citations found.

    Similar books and articles

    Monthly downloads

    Added to index


    Total downloads

    2 ( #258,346 of 1,089,053 )

    Recent downloads (6 months)

    1 ( #69,801 of 1,089,053 )

    How can I increase my downloads?

    My notes
    Sign in to use this feature

    Start a new thread
    There  are no threads in this forum
    Nothing in this forum yet.