Rotting Infinitely Many-armed Bandits
This addresses a theoretical challenge in online learning with non-stationary rewards, providing algorithms for scenarios where arm quality decays, which is incremental as it extends prior bandit models.
The paper tackles the infinitely many-armed bandit problem with rotting rewards, where arm rewards decrease over time, and establishes a worst-case regret lower bound of Ω(max{ϱ^{1/3}T, √T}) and matching upper bounds with algorithms using UCB indices and thresholds.
We consider the infinitely many-armed bandit problem with rotting rewards, where the mean reward of an arm decreases at each pull of the arm according to an arbitrary trend with maximum rotting rate $\varrho=o(1)$. We show that this learning problem has an $Ω(\max\{\varrho^{1/3}T,\sqrt{T}\})$ worst-case regret lower bound where $T$ is the horizon time. We show that a matching upper bound $\tilde{O}(\max\{\varrho^{1/3}T,\sqrt{T}\})$, up to a poly-logarithmic factor, can be achieved by an algorithm that uses a UCB index for each arm and a threshold value to decide whether to continue pulling an arm or remove the arm from further consideration, when the algorithm knows the value of the maximum rotting rate $\varrho$. We also show that an $\tilde{O}(\max\{\varrho^{1/3}T,T^{3/4}\})$ regret upper bound can be achieved by an algorithm that does not know the value of $\varrho$, by using an adaptive UCB index along with an adaptive threshold value.