We consider online no-regret learning in unknown games with bandit feedback, where each agent only observes its reward at each time -- determined by all players' current joint action -- rather than its gradient. We focus on the class of smooth and strongly monotone games and study optimal...

This paper proposes a general framework/meta-policy to solve Revenue Management (RM) problems with demand learning and potentially large action space, constrained by initial unreplenishable resources. This framework combines the technique of primal-dual method in optimization and...