  We propose a stochastic first-order algorithm to learn the rationality
parameters of simultaneous and non-cooperative potential games, i.e., the
parameters of the agents' optimization problems. Our technique combines (i.) an
active-set step that enforces that the agents play at a Nash equilibrium and
(ii.) an implicit-differentiation step to update the estimates of the
rationality parameters. We detail the convergence properties of our algorithm
and perform numerical experiments on Cournot and congestion games, showing that
our algorithm effectively finds high-quality solutions (in terms of
out-of-sample loss) and scales to large datasets.
