Hierarchical Solution of Markov Decision Processes using Macro-actions
  We investigate the use of temporally abstract actions, or macro-actions, in
the solution of Markov decision processes. Unlike current models that combine
both primitive actions and macro-actions and leave the state space unchanged,
we propose a hierarchical model (using an abstract MDP) that works with
macro-actions only, and that significantly reduces the size of the state space.
This is achieved by treating macroactions as local policies that act in certain
regions of state space, and by restricting states in the abstract MDP to those
at the boundaries of regions. The abstract MDP approximates the original and
can be solved more efficiently. We discuss several ways in which macro-actions
can be generated to ensure good solution quality. Finally, we consider ways in
which macro-actions can be reused to solve multiple, related MDPs; and we show
that this can justify the computational overhead of macro-action generation.
