Robust Hierarchical Planning with Policy Delegation
  We propose a novel framework and algorithm for hierarchical planning based on
the principle of delegation. This framework, the Markov Intent Process,
features a collection of skills which are each specialised to perform a single
task well. Skills are aware of their intended effects and are able to analyse
planning goals to delegate planning to the best-suited skill. This principle
dynamically creates a hierarchy of plans, in which each skill plans for
sub-goals for which it is specialised. The proposed planning method features
on-demand execution---skill policies are only evaluated when needed. Plans are
only generated at the highest level, then expanded and optimised when the
latest state information is available. The high-level plan retains the initial
planning intent and previously computed skills, effectively reducing the
computation needed to adapt to environmental changes. We show this planning
approach is experimentally very competitive to classic planning and
reinforcement learning techniques on a variety of domains, both in terms of
solution length and planning time.
