We develop an algorithm for opponent modeling in large extensive-form games of imperfect information. It works by observing the opponent’s action frequencies and building an opp...
We study on-line play of repeated matrix games in which the observations of past actions of the other player and the obtained reward are partial and stochastic. We define the Part...
In addressing the challenge of exponential scaling with the number of agents we adopt a cluster-based representation to approximately solve asymmetric games of very many players. ...
Young people’s participation in online digital culture is one of the most efficient means by which they become proficient in the management of Information and Communications Tec...
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...