Adaptive Envelope MDPs for Relational Equivalence-based Planning
We describe a method to use structured representations of the environmentâ s dynamics to constrain and speed up the planning process. Given a problem domain described in a probabilistic logical description language, we develop an anytime technique that incrementally improves on an initial, partial policy. This partial solution is found by ï¬ rst reducing the number of predicates needed to represent a relaxed version of the problem to a minimum, and then dynamically partitioning the action space into a set of equivalence classes with respect to this minimal representation. Our approach uses the envelope MDP framework, which creates a Markov decision process out of a subset of the full state space as de- termined by the initial partial solution. This strategy permits an agent to begin acting within a restricted part of the full state space and to expand its envelope judiciously as resources permit.