On the Linear Belief Compression of POMDPs: A re-examination of current methods

5 Aug 2015Zhuoran WangPaul A. CrookWenshuo TangOliver Lemon

Belief compression improves the tractability of large-scale partially observable Markov decision processes (POMDPs) by finding projections from high-dimensional belief space onto low-dimensional approximations, where solving to obtain action selection policies requires fewer computations. This paper develops a unified theoretical framework to analyse three existing linear belief compression approaches, including value-directed compression and two non-negative matrix factorisation (NMF) based algorithms... (read more)

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