The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs
This addresses a critical challenge in practical applications where input changes incur costs, offering a plug-in solution for broader bandit settings.
The paper tackles the problem of Bayesian Optimization with movement costs by introducing a framework that integrates with batched algorithms, providing theoretical convergence guarantees and empirically reducing average movement costs while maintaining comparable regret to conventional methods.
This paper introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs. Our approach is a convenient plug-in that seamlessly integrates with the existing literature on batched algorithms, where designs within batches are observed following the solution of a Traveling Salesman Problem. The proposed method provides a theoretical guarantee of convergence in terms of movement costs for BO. Empirically, our method effectively reduces average movement costs over time while maintaining comparable regret performance to conventional BO methods. This framework also shows promise for broader applications in various bandit settings with movement costs.