Incentivized Lipschitz Bandits
This work is significant for principals in continuous MAB settings who need to incentivize exploration while managing reward drift and compensation costs, offering a method to achieve both sublinear regret and compensation.
This paper addresses incentivized exploration in multi-armed bandit (MAB) settings with infinitely many arms, where a principal compensates myopic agents to explore beyond greedy choices, leading to reward drift. The proposed algorithms achieve sublinear cumulative regret and sublinear total compensation, with regret and compensation bounds of O(T^(d+1/d+2)) for a covering dimension d.
We study incentivized exploration in multi-armed bandit (MAB) settings with infinitely many arms modeled as elements in continuous metric spaces. Unlike classical bandit models, we consider scenarios where the decision-maker (principal) incentivizes myopic agents to explore beyond their greedy choices through compensation, but with the complication of reward drift--biased feedback arising due to the incentives. We propose novel incentivized exploration algorithms that discretize the infinite arm space uniformly and demonstrate that these algorithms simultaneously achieve sublinear cumulative regret and sublinear total compensation. Specifically, we derive regret and compensation bounds of $\Tilde{O}(T^{d+1/d+2})$, with $d$ representing the covering dimension of the metric space. Furthermore, we generalize our results to contextual bandits, achieving comparable performance guarantees. We validate our theoretical findings through numerical simulations.