In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an interaction. This work focuses on the problem of recognizing the strategy used by an agent within a small number of interactions. We propose using a Bayesian framework to address this problem. In this paper we extend Bayesian Policy Reuse to adversarial settings where opponents switch from one stationary strategy to another. Our extension en- ables online learning of new models when the learning agent detects that the current policies are not performing optimally. Experiments presented in repeated games show that our approach yields better performance than state-of-the-art approaches in terms of average rewards.