Incremental methods for computing bounds in partially observable Markov decision processes

• Authors: Milos Hauskrecht
• Venue: AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
• Year: 1997

Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect observability. The control problem is formulated as a dynamic optimization problem with a value function combining costs or rewards from multiple steps. In this paper we propose, analyse and test various incremental methods for computing bounds on the value function for control problems with infinite discounted horizon criteria. The methods described and tested include novel incremental versions of grid-based linear interpolation method and simple lower bound method with Sondik's updates. Both of these can work with arbitrary points of the belief space and can be enhanced by various heuristic point selection strategies. Also introduced is a new method for computing an initial upper bound - the fast informed bound method. This method is able to improve significantly on the standard and commonly used upper bound computed by the MDP-based method. The quality of resulting bounds are tested on a maze navigation problem with 20 states, 6 actions and 8 observations.