Region-Based Approximations for Planning in Stochastic Domains

02/06/2013 ∙ by Nevin Lianwen Zhang, et al. ∙ 0

This paper is concerned with planning in stochastic domains by means of partially observable Markov decision processes (POMDPs). POMDPs are difficult to solve. This paper identifies a subclass of POMDPs called region observable POMDPs, which are easier to solve and can be used to approximate general POMDPs to arbitrary accuracy.

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