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We address the problem of approximate model minimization for MDPs in whichthe state is partitioned into endogenous and (much larger) exogenouscomponents. An exogenous state variable is one whose dynamics are independentof the agent's actions. We formalize the mask-learning problem, in which theagent must choose a subset of exogenous state variables to reason about whenplanning; doing planning in such a reduced state space can often besignificantly more efficient than planning in the full model. We then explorethe various value functions at play within this setting, and describeconditions under which a policy for a reduced model will be optimal for thefull MDP. The analysis leads us to a tractable approximate algorithm that drawsupon the notion of mutual information among exogenous state variables. Wevalidate our approach in simulated robotic manipulation domains where a robotis placed in a busy environment, in which there are many other agents alsointeracting with the objects. Visit http://tinyurl.com/chitnis-exogenous for asupplementary video.

Learning Compact Models for Planning with Exogenous Processes