A POMDP approximation algorithm that anticipates the need to observe
By: Valentina Bayer, Thomas Dietterich.
Contributors: Dietterich, Thomas Glen.; Oregon State University. Dept. of Computer Science.
Series: [Technical report -- 00-30-01]; Technical report (Oregon State University. Dept. of Computer Science) -- 00-30-01.
Publisher: Oregon State University, Dept. of Computer Science
Published: 2000
Place of publication: [Corvallis, OR
Language: eng
Description: This paper introduces the even-odd POMDP, an approximation to POMDPs in which the world is assumed to be fully observable every other time step. 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 POMDP's 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 gives better policies, and we show that it can find a good policy for a POMDP with 10,000 states and observations.
Notes: Caption title.

Includes bibliographical references (leaf [8]).

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