Online Learning for Cooperative Multi-Player Multi-Armed Bandits. (arXiv:2109.03818v1 [cs.LG])

We introduce a framework for decentralized online learning for multi-armed
bandits (MAB) with multiple cooperative players. The reward obtained by the
players in each round depends on the actions taken by all the players. It’s a
team setting, and the objective is common. Information asymmetry is what makes
the problem interesting and challenging. We consider three types of information
asymmetry: action information asymmetry when the actions of the players can’t
be observed but the rewards received are common; reward information asymmetry
when the actions of the other players are observable but rewards received are
IID from the same distribution; and when we have both action and reward
information asymmetry. For the first setting, we propose a UCB-inspired
algorithm that achieves $O(log T)$ regret whether the rewards are IID or
Markovian. For the second section, we offer an environment such that the
algorithm given for the first setting gives linear regret. For the third
setting, we show that a variation of the `explore then commit’ algorithm
achieves almost log regret.



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