Linear Program Approximations for Factored Continuous-State Markov Decision Processes

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Milos Hauskrecht, Branislav Kveton


Approximate linear programming (ALP) has emerged recently as one of the most promising methods for solving complex factored MDPs with (cid:2)nite state spaces. In this work we show that ALP solutions are not limited only to MDPs with (cid:2)nite state spaces, but that they can also be applied successfully to factored continuous-state MDPs (CMDPs). We show how one can build an ALP-based approximation for such a model and contrast it to existing solution methods. We argue that this approach offers a robust alternative for solving high dimensional continuous-state space problems. The point is supported by experiments on three CMDP problems with 24-25 continuous state factors.