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Belief State Analysis for POMDP Problems

Learn about solving large-scale POMDP problems through belief state analysis using spatio-temporal clustering and dimension reduction techniques. Compare policy quality with conventional methods.

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Belief State Analysis for POMDP Problems

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  1. MDP + Random Sampling to Collect Beliefs Temporal difference Spatial difference Solving Large-Scale POMDP Problems Via Belief State Analysis Xin Li, William K. Cheung, Jiming Liu Observation: Wall’s combination! Where am I on earth? How to get to the room with ? Which is the next better action? ? ? ? Policy quality comparison between the conventional belief compression and the proposed method with belief clustering Clustering Sub-policy1 Dimension reduction with EPCA per cluster Policy Low-dimensional space Discretization High-dimensional beliefspace Sub-policy2 X. Li, W. K. Cheung, J. Liu, "Towards Solving Large-Scale POMDP Problems via Spatio-Temporal Brief State Clustering," Proceedings of IJCAI-05 Workshop on Reasoning with Uncertainty in Robotics (RUR-05), July 2005

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