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A POMDP approximation algorithm that anticipates the need to observe Public Deposited

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https://ir.library.oregonstate.edu/concern/technical_reports/gm80hw69f

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  • This paper introduces the even-odd POMDP an approximation to POMDPs Partially Observable Markov Decision Problems in which the world is assumed to be fully observable every other time step. This approximation works well for problems with a delayed need to observe. The even-odd POMDP can be converted into an equivalent MDP the 2MDP whose value function, V*[subscript 2MDP], can be combined online with a 2-step lookahead search to provide a good POMDP policy. We prove that this gives an approximation to the POMDPs optimal value function that is at least as good as methods based on the optimal value function of the underlying MDP. We present experimental evidence that the method finds a good policy for a POMDP with states and observations.
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  • This research was supported by AFOSR F49620-9810375.
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  • description.provenance : Made available in DSpace on 2012-05-30T22:39:04Z (GMT). No. of bitstreams: 1 A POMDP approximation algorithm that anticipates the need to observe 2004.pdf: 194701 bytes, checksum: 18f62ad50546e3db997d1d1775a96667 (MD5) Previous issue date: 2004-07-05
  • description.provenance : Submitted by Laura Wilson (laura.wilson@oregonstate.edu) on 2012-05-30T22:37:33Z No. of bitstreams: 1 A POMDP approximation algorithm that anticipates the need to observe 2004.pdf: 194701 bytes, checksum: 18f62ad50546e3db997d1d1775a96667 (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2012-05-30T22:39:04Z (GMT) No. of bitstreams: 1 A POMDP approximation algorithm that anticipates the need to observe 2004.pdf: 194701 bytes, checksum: 18f62ad50546e3db997d1d1775a96667 (MD5)

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