belief-point pomdp
Applying Metric-Trees to Belief-Point POMDPs
Recent developments in grid-based and point-based approximation algo- rithms for POMDPs have greatly improved the tractability of POMDP planning. These approaches operate on sets of belief points by individ- ually learning a value function for each point. In reality, belief points exist in a highly-structured metric simplex, but current POMDP algo- rithms do not exploit this property. This paper presents a new metric-tree algorithm which can be used in the context of POMDP planning to sort belief points spatially, and then perform fast value function updates over groups of points.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (1.00)