Linear Program Approximations for Factored Continuous-State Markov Decision Processes

Approximate linear programming (ALP) has emerged recently as one of the most promising methods for solving complex factored MDPs with finite state spaces. In this work we show that ALP solutions are not limited only to MDPs with finite 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.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Milos Hauskrecht, Branislav Kveton
• Keywords: Approximate Linear Programming, Complex Factored Mdps, NIPS 2003, NIPS 2007, finite State Spaces