Region-Based Approximations for Planning in Stochastic Domains

Published 6 Feb 2013 in cs.AI | (1302.1573v1)

Abstract: 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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