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UCB, Thompson sampling etc seems myopic/greedy for bandits?

The (binary) multi-armed bandit actually is a MDP with one state and $K$ actions. So your suggestion boils down to meta-learning: Find the parameters of one MDP that can solve another. Let's go with ...
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