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Although recent work in AI has made great progress in solving large,zero-sum, extensive-form games, the underlying assumption in most past work isthat the parameters of the game itself are known to the agents. This paperdeals with the relatively under-explored but equally important "inverse"setting, where the parameters of the underlying game are not known to allagents, but must be learned through observations. We propose a differentiable,end-to-end learning framework for addressing this task. In particular, weconsider a regularized version of the game, equivalent to a particular form ofquantal response equilibrium, and develop 1) a primal-dual Newton method forfinding such equilibrium points in both normal and extensive form games; and 2)a backpropagation method that lets us analytically compute gradients of allrelevant game parameters through the solution itself. This ultimately lets uslearn the game by training in an end-to-end fashion, effectively by integratinga "differentiable game solver" into the loop of larger deep networkarchitectures. We demonstrate the effectiveness of the learning method inseveral settings including poker and security game tasks.

What game are we playing? End-to-end learning in normal and extensive form games