 Learning of equilibria may take various forms for different agents and for games of differing levels of complexity and risk. Incorporating it into game-theoretic models of interactions thus introduces an extensive new set of technicalities. For the most fully developed general theory, the reader is referred to  Fudenberg and Levine (1998);  the same authors provide a non-technical overview of the issues in  Fudenberg and Levine (2016)).  A first important distinction is between learning specific parameters between rounds of a repeated game (see  Section 4)  with common players, and learning about general strategic expectations across different games. The latter can include learning about players if the learner is updating expectations based on her models of types of players she recurrently encounters. Then we can distinguish between passive learning, in which a player merely updates her subjective priors based on her observation of moves and outcomes, and strategic choices she infers from these, and active learning, in which she probes—in technical language screens—for information about other players’ strategies by choosing strategies that test her conjectures about what will occur off what she believes to be the game’s equilibrium path. A major difficulty for both players and modelers is that screening moves might be misinterpreted if players are also incentivized to make moves to signal information to one another (see  Section 4).  In other words: trying to learn about strategies can under some circumstances interfere with players’ abilities to learn equilibria. Finally, the discussion so far has assumed that all possible learning in a game is about the structure of the game itself.  Wilcox (2008)  shows that if players are learning new information about causal processes occurring outside a game while simultaneously trying to update expectations about other players’ strategies, the modeler can find herself reaching beyond the current limits of technical knowledge.