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- W3101822862 abstract "Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent's belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other point-based methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems." @default.
- W3101822862 created "2020-11-23" @default.
- W3101822862 creator A5075395956 @default.
- W3101822862 creator A5082325811 @default.
- W3101822862 date "2005-08-01" @default.
- W3101822862 modified "2023-10-14" @default.
- W3101822862 title "Perseus: Randomized Point-based Value Iteration for POMDPs" @default.
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- W3101822862 doi "https://doi.org/10.1613/jair.1659" @default.
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