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When speed matters in learning against adversarial opponents

We propose a novel algorithm that is able to learn and adapt to an opponent even within a limited number of interactions and against a rapidly adapting opponent. The context we use is two player normal form games. We compare the performance of an agent using our algorithm against agents using existing multiagent learning algorithms.