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In this work we investigate an importance sampling approach for evaluatingpolicies for a structurally time-varying factored Markov decision process(MDP), i.e. the policy's value is estimated with a high-probability confidenceinterval. In particular, we begin with a multi-agent MDP controlled by a knownpolicy but with unknown transition dynamics. One agent is then removed from thesystem - i.e. the system experiences node dropout - forming a new MDP of theremaining agents, with a new state space, action space, and new transitiondynamics. We assume that the effect of removing an agent corresponds to themarginalization of its factor in the transition dynamics. The reward functionmay likewise be marginalized, or it may be entirely redefined for the newsystem. Robust policy importance sampling is then used to evaluate candidatepolicies for the new system, and estimated values are presented withprobabilistic confidence bounds. This computation is completed with noobservations of the new system, meaning that a safe policy may be found beforedropout occurs. The utility of this approach is demonstrated in simulation andcompared to Monte Carlo simulation of the new system.

Corrected: On Confident Policy Evaluation for Factored Markov Decision Processes with Node Dropouts