Region-Based Approximations for Planning in Stochastic Domains
Nevin Lianwen Zhang, Wenju Liu · arXiv (Cornell University) · 2013
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.