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Research on numerical solution methods for partially observable Markov decision processes (POMDPs) has primarily focused on finite-state models, and these algorithms do not generally extend to continuous-state POMDPs, due to the infinite dimensionality of the belief space. In this paper, we develop a computationally viable and theoretically sound method for solving continuous-state POMDPs by effectively...
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