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The paper introduces a vector Bellman theory for robust average-reward Markov decision processes, enabling optimal performance under transition uncertainty with finite model solvability and an approximately shifted Halpern planning algorithm.
This paper studies online cooperative control of multi-agent systems under hidden Byzantine attacks, establishing information-theoretic limits and proposing a robust estimation-to-decisions learner with provable regret bounds.