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Perseus: Randomized Point-based Value Iteration for POMDPs
2005
The Journal of Artificial Intelligence Research
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
doi:10.1613/jair.1659
fatcat:tij66jweknbltfpjampdk2zkk4