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Arxiv
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Regret Bounds for Robust Online Decision Making
- A framework is proposed for robust online decision making, allowing multivalued models.
- The framework introduces convex sets of probability distributions for decision outcomes.
- Nature can choose distributions from the set in an arbitrary and non-oblivious manner.
- The framework demonstrates improved regret bounds in robust linear bandits and tabular robust online reinforcement learning.
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