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Vector Bellman Theory for Multichain Robust Average-Reward Markov Decision Processes
Robust average-reward Markov decision processes provide a fundamental framework for long-term performance optimization under uncertainty, and can have optimal long-run rewards that depend on the initial state. This state dependence requires a vector Bellman theory that accounts for both recurrent-cl
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