  We study an approach to policy selection for large relational Markov Decision
Processes (MDPs). We consider a variant of approximate policy iteration (API)
that replaces the usual value-function learning step with a learning step in
policy space. This is advantageous in domains where good policies are easier to
represent and learn than the corresponding value functions, which is often the
case for the relational MDPs we are interested in. In order to apply API to
such problems, we introduce a relational policy language and corresponding
learner. In addition, we introduce a new bootstrapping routine for goal-based
planning domains, based on random walks. Such bootstrapping is necessary for
many large relational MDPs, where reward is extremely sparse, as API is
ineffective in such domains when initialized with an uninformed policy. Our
experiments show that the resulting system is able to find good policies for a
number of classical planning domains and their stochastic variants by solving
them as extremely large relational MDPs. The experiments also point to some
limitations of our approach, suggesting future work.
