An Algorithm for Stochastic and Adversarial Bandits with Switching Costs

TL;DR

This paper introduces a versatile algorithm for multiarmed bandits with switching costs, achieving optimal regret bounds in both stochastic and adversarial settings without prior knowledge of the environment.

Contribution

It adapts the Tsallis-INF algorithm to handle switching costs, providing minimax optimal regret bounds across different regimes and extending to time-varying switching costs.

Findings

Achieves minimax optimal regret bounds in adversarial and stochastic regimes.

Performs competitively with baseline algorithms in various settings.

Handles environments with changing switching costs over time.

Abstract

We propose an algorithm for stochastic and adversarial multiarmed bandits with switching costs, where the algorithm pays a price $\lambda$ every time it switches the arm being played. Our algorithm is based on adaptation of the Tsallis-INF algorithm of Zimmert and Seldin (2021) and requires no prior knowledge of the regime or time horizon. In the oblivious adversarial setting it achieves the minimax optimal regret bound of $O\big((\lambda K)^{1/3}T^{2/3} + \sqrt{KT}\big)$, where $T$ is the time horizon and $K$ is the number of arms. In the stochastically constrained adversarial regime, which includes the stochastic regime as a special case, it achieves a regret bound of $O\left(\big((\lambda K)^{2/3} T^{1/3} + \ln T\big)\sum_{i \neq i^*} \Delta_i^{-1}\right)$, where $\Delta_i$ are the suboptimality gaps and $i^*$ is a unique optimal arm. In the special case of $\lambda = 0$ (no switching costs), both bounds are minimax optimal within constants. We also explore variants of the problem, where switching cost is allowed to change over time. We provide experimental evaluation showing competitiveness of our algorithm with the relevant baselines in the stochastic, stochastically constrained adversarial, and adversarial regimes with fixed switching cost.