An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability
An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability explores A novel policy for multi-armed bandits that optimizes exploration in dynamic environments with side-observations and stochastic availability.. Commercial viability score: 4/10 in Reinforcement Learning.
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