Learning and teaching in repeated games: A machine learning approach to long-term best-response play
Repeated games and long-term best-response play. We consider the problem of learning during a play of a a repeated game. Existing approaches often focus on algorithms that asymptotically achieve an equilibrium of the one-shot game. We shift away from that paradigm to consider learning strategies that attempt to play long-term best-response to the opponent’s observed strategy. This goal may be more appropriate for several reasons: