Online Learning for Cooperative Multi-Player Multi-Armed Bandits
This addresses coordination challenges in multi-agent systems like robotics or networking, but it is incremental as it builds on existing bandit frameworks with new asymmetry types.
The paper tackles decentralized online learning for cooperative multi-player multi-armed bandits with information asymmetry, proposing algorithms that achieve O(log T) regret for action information asymmetry and almost log regret for combined asymmetries, while showing linear regret in reward information asymmetry.
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.