  In this paper, we study the problem of learning the set of pure strategy Nash
equilibria and the exact structure of a continuous-action graphical game with
quadratic payoffs by observing a small set of perturbed equilibria. A
continuous-action graphical game can possibly have an uncountable set of Nash
euqilibria. We propose a $\ell_{12}-$ block regularized method which recovers a
graphical game, whose Nash equilibria are the $\epsilon$-Nash equilibria of the
game from which the data was generated (true game). Under a slightly stringent
condition on the parameters of the true game, our method recovers the exact
structure of the graphical game. Our method has a logarithmic sample complexity
with respect to the number of players. It also runs in polynomial time.
