Belief-node condensation for online POMDP algorithms
Gavin Rens, Alexander Ferrein · 2013
We consider online partially observable Markov decision processes (POMDPs) which compute policies by local look-ahead from the current belief-state. One problem is that belief-nodes deeper in the decision-tree increase in the number of states with non-zero probability they contain. Computation time of updating a belief-state is exponential in the number of states contained by the belief. Belief-update occurs for each node in a search tree. It would thus pay to reduce the size of the nodes while keeping the information they contain. In this paper, we compare four fast and frugal methods to reduce the size of belief-nodes in the search tree, hence improving the running-time of online POMDP algorithms.