  We propose a novel master-slave architecture to solve the top-$K$
combinatorial multi-armed bandits problem with non-linear bandit feedback and
diversity constraints, which, to the best of our knowledge, is the first
combinatorial bandits setting considering diversity constraints under bandit
feedback. Specifically, to efficiently explore the combinatorial and
constrained action space, we introduce six slave models with distinguished
merits to generate diversified samples well balancing rewards and constraints
as well as efficiency. Moreover, we propose teacher learning based optimization
and the policy co-training technique to boost the performance of the multiple
slave models. The master model then collects the elite samples provided by the
slave models and selects the best sample estimated by a neural contextual
UCB-based network to make a decision with a trade-off between exploration and
exploitation. Thanks to the elaborate design of slave models, the co-training
mechanism among slave models, and the novel interactions between the master and
slave models, our approach significantly surpasses existing state-of-the-art
algorithms in both synthetic and real datasets for recommendation tasks. The
code is available at:
\url{https://github.com/huanghanchi/Master-slave-Algorithm-for-Top-K-Bandits}.
