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We consider the restless multiarmed bandit problem with unknown dynamics in which a player chooses one out of arms to play at each time. The reward state of each arm transits according to an unknown Markovian rule when it is played and evolves according to an arbitrary unknown random process when it is passive. The performance of an arm selection policy is measured by regret, defined as the reward...
We consider a class of restless multi-armed bandit (RMAB) problems, in which the active action resets the stochastic evolution of the system. We obtain the Whittle index in closed-form, showing that it induces a policy that is equivalent to the myopic policy, and that it is optimal for stochastically identical arms. These results find applications in opportunistic spectrum access and supervisory control...
We consider decentralized restless multi-armed bandit problems with unknown dynamics and multiple players. The reward state of each arm transits according to an unknown Markovian rule when it is played and evolves according to an arbitrary unknown random process when it is passive. Players activating the same arm at the same time collide and suffer from reward loss. The objective is to maximize the...
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