3 accepted papers
In this paper, we study a multi-agent bandit problem in an unknown general-sum game repeated for a number of rounds (i.e., learning in a black-box game with bandit feedback), where a set of agents have no information about the underlying game structure and cannot observe each other’s actions and rew…
With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and o…
This paper addresses the stochastic multi-armed bandit problem with an undirected feedback graph. We devise a UCB-based algorithm, UCB-NE, to provide a problem-dependent regret bound that depends on a clique covering. Our algorithm obtains regret which provably scales linearly with the clique coveri…