We provide a uniform framework for learning against a recent history adversary in arbitrary repeated bimatrix games, by modeling such an agent as a Markov Decision Process. We focus on learning an optimal non-stationary policy in such an MDP over a finite horizon and adapt an existing efficient Monte Carlo based algorithm for learning optimal policies in such MDPs. We show that this new efficie...