Non-myopic learning in repeated stochastic games

30 Sep 2014Jacob W. Crandall

In repeated stochastic games (RSGs), an agent must quickly adapt to the behavior of previously unknown associates, who may themselves be learning. This machine-learning problem is particularly challenging due, in part, to the presence of multiple (even infinite) equilibria and inherently large strategy spaces... (read more)

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