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For questions about the contextual bandit (CB) problem and algorithms that solve it. The CB problem is a generalization of the (context-free) multi-armed bandit problem, where there is more than one situation (or state) and the optimal action to take in one state may be different than the optimal action to take in another state, but where the actions do not affect states (as e.g. in the reinforcement learning problem), but only the rewards.

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