Inductive Policy Selection for First-Order MDPs
  We select policies for large Markov Decision Processes (MDPs) with compact
first-order representations. We find policies that generalize well as the
number of objects in the domain grows, potentially without bound. Existing
dynamic-programming approaches based on flat, propositional, or first-order
representations either are impractical here or do not naturally scale as the
number of objects grows without bound. We implement and evaluate an alternative
approach that induces first-order policies using training data constructed by
solving small problem instances using PGraphplan (Blum & Langford, 1999). Our
policies are represented as ensembles of decision lists, using a taxonomic
concept language. This approach extends the work of Martin and Geffner (2000)
to stochastic domains, ensemble learning, and a wider variety of problems.
Empirically, we find "good" policies for several stochastic first-order MDPs
that are beyond the scope of previous approaches. We also discuss the
application of this work to the relational reinforcement-learning problem.
