  This paper presents a way of solving Markov Decision Processes that combines
state abstraction and temporal abstraction. Specifically, we combine state
aggregation with the options framework and demonstrate that they work well
together and indeed it is only after one combines the two that the full benefit
of each is realized. We introduce a hierarchical value iteration algorithm
where we first coarsely solve subgoals and then use these approximate solutions
to exactly solve the MDP. This algorithm solved several problems faster than
vanilla value iteration.
