How Much Learning Is Sufficient in Interference Games?

This paper studies the learning behavior of self-interested users interacting in a two-user OR-channel interference game. We discuss how a strategic user should learn the behavior of its opponent, adapt its actions, and improve its own performance. Specifically, we investigate the tradeoff that can be made by a user between learning duration and performance, if the opponent plays a mixed strategy. First, we assume a stationary opponent and we apply optimization theory and large deviations theory to analytically derive an upper bound of the minimum training required by the user given the tolerable performance loss and outage probability. Next, we extend the results to the cases, where an adaptive opponent plays a conditional strategy based on its bounded memory. By solving linear programs, we design optimized learning strategies that minimize an upper bound of the duration of learning against the adaptive opponent.