algorithm 5
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Context-lumpable stochastic bandits
Consider a recommendation platform that interacts with a finite set of users in an online fashion. Users arrive at the platform and receive a recommendation.
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Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
Our approach combines three key ideas: (1) value-demand augmentation, (2) action-space approximate dynamic programming, and (3) time-space rounding.
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