Low-complexity algorithm for restless bandits with imperfect observations

We consider a class of restless bandit problems that finds a broad application area in reinforcement learning and stochastic optimization. We consider N independent discrete-time Markov processes, each of which had two possible states: 1 and 0 (‘good’ and ‘bad’). Only if a process is both in state 1...

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