Inductive Policy Selection for First-Order MDPs

Understand

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).