Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits
NIPS 2020, 2020.
We study the online restless bandit problem, where the state of each arm evolves according to a Markov chain, and the reward of pulling an arm depends on both the pulled arm and the current state of the corresponding Markov chain. In this paper, we propose Restless-UCB, a learning policy that follows the explore-then-commit framework. I...More
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