Fast Learning against Adaptive Adversarial Opponents
Mohamed Elidrisi, Nicholas Johnson, Maria L. Gini · 2013
Theabilitytolearnandadaptwhenplayingagainstanadaptive opponent requires the ability to predict the opponent’s behavior. Capturing any changes in the opponent’s behavior during a sequence of plays is critical to achieve positive outcomes in such an environment. We identify two new requirements that we suggest are essentialforagentsthatlearninadaptiveenvironments. These requirements are dictated by the fact that repeated interactions in practice have to be limited and that the opponent can rapidly change strategy through the sequence of interactions. We believe that building intelligent agents that can survive in environments with such requirements will lead to wider deployment of learning agents. We propose a novel algorithm that is able to learn and adapt rapidly to an opponent even when the number of interactions is limited and the opponentis adaptingquickly by changing its strategy. The context we use for the experimental work is two player normal form games. We compare the performance of an agent using our algorithm against agents using existing multiagent learning algorithms. 1.