An α-No-Regret Algorithm For Graphical Bilinear Bandits

We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where n agents in a graph play a stochastic bilinear bandit game with each of their neighbors. This setting reveals a combinatorial NP-hard problem that prevents the use of any existing regret-based algorithm in the (bi-)linear bandit literature. In this paper, we fill this gap and present the first regret-based algorithm for graphical bilinear bandits using the principle of optimism in the face of uncertainty. Theoretical analysis of this new method yields an upper bound of tildeO(√T) on the α-regret and evidences the impact of the graph structure on the rate of convergence. Finally, we show through various experiments the validity of our approach.