AbsIrac-We explore the advantages of representing hier. archical partially observable Markov decision processes (HPOMDPs) as dynamic Bayesian networks (DBNs). In particular, we focus on the special case of using H-POMDPs to represent multi-resolution spatial maps for indoor robot navigation. Our results show that a DBN representation of H-POMDPs can train significantly faster than the original learning algorithm for HPOMDPs or the equivalent flat POMDP, and requires much less data. In addition, the DBN formulation can easily be extended to parameter tying and factoring of variables, which further reduces the time and sample complexity. This enables us to apply H-POMDP methods to much larger problems than prerionsly possible.
The MDP+POMDP algorithm is representationally similar to using a dynamic Bayesian network (DBN) representation of hierarchical POMDPs REF .
Representing hierarchical POMDPs as DBNs for multi-scale robot localization
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
Computer Science