Combinatorial Semi-Bandit in the Non-Stationary Environment
This addresses the challenge of decision-making under uncertainty in dynamic environments for applications like online advertising or recommendation systems, with incremental improvements in algorithm design.
The paper tackles the non-stationary combinatorial semi-bandit problem, achieving nearly optimal regret bounds of $ ilde{\mathcal{O}}(\sqrt{\mathcal{S} T})$ in the switching case and $ ilde{\mathcal{O}}(\mathcal{V}^{1/3}T^{2/3})$ in the dynamic case for general scenarios, and develops a parameter-free algorithm for a linear reward special case.
In this paper, we investigate the non-stationary combinatorial semi-bandit problem, both in the switching case and in the dynamic case. In the general case where (a) the reward function is non-linear, (b) arms may be probabilistically triggered, and (c) only approximate offline oracle exists \cite{wang2017improving}, our algorithm achieves $\tilde{\mathcal{O}}(\sqrt{\mathcal{S} T})$ distribution-dependent regret in the switching case, and $\tilde{\mathcal{O}}(\mathcal{V}^{1/3}T^{2/3})$ in the dynamic case, where $\mathcal S$ is the number of switchings and $\mathcal V$ is the sum of the total ``distribution changes''. The regret bounds in both scenarios are nearly optimal, but our algorithm needs to know the parameter $\mathcal S$ or $\mathcal V$ in advance. We further show that by employing another technique, our algorithm no longer needs to know the parameters $\mathcal S$ or $\mathcal V$ but the regret bounds could become suboptimal. In a special case where the reward function is linear and we have an exact oracle, we design a parameter-free algorithm that achieves nearly optimal regret both in the switching case and in the dynamic case without knowing the parameters in advance.