Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources
This addresses a resource-constrained preference learning problem for applications like online recommendations, but it is incremental as it builds on existing dueling bandit methods.
The paper tackles the problem of reward maximization in dueling bandits with resource consumption constraints, showing that without assumptions the problem is not learnable, and proposes an EXP3-based algorithm achieving an $ ilde{\mathcal{O}}\left({ rac{OPT^{(b)}}{B}}K^{1/3}T^{2/3} ight)$ regret bound.
We consider the problem of reward maximization in the dueling bandit setup along with constraints on resource consumption. As in the classic dueling bandits, at each round the learner has to choose a pair of items from a set of $K$ items and observe a relative feedback for the current pair. Additionally, for both items, the learner also observes a vector of resource consumptions. The objective of the learner is to maximize the cumulative reward, while ensuring that the total consumption of any resource is within the allocated budget. We show that due to the relative nature of the feedback, the problem is more difficult than its bandit counterpart and that without further assumptions the problem is not learnable from a regret minimization perspective. Thereafter, by exploiting assumptions on the available budget, we provide an EXP3 based dueling algorithm that also considers the associated consumptions and show that it achieves an $\tilde{\mathcal{O}}\left({\frac{OPT^{(b)}}{B}}K^{1/3}T^{2/3}\right)$ regret, where $OPT^{(b)}$ is the optimal value and $B$ is the available budget. Finally, we provide numerical simulations to demonstrate the efficacy of our proposed method.