Focussed Propagation of MDPs for Path Planning

We present a heuristic-based algorithm for solving restricted Markov decision processes (MDPs). Our approach, which combines ideas from deterministic search and recent dynamic programming methods, focusses computation towards promising areas of the state space. It is thus able to significantly reduce the amount of processing required to produce a solution. We demonstrate this improvement by comparing the performance of our approach to the performance of several existing algorithms on a robotic path planning domain.