  Understanding and analyzing markets is crucial, yet analytical equilibrium
solutions remain largely infeasible. Recent breakthroughs in equilibrium
computation rely on zeroth-order policy gradient estimation. These approaches
commonly suffer from high variance and are computationally expensive. The use
of fully differentiable simulators would enable more efficient gradient
estimation. However, the discrete allocation of goods in economic simulations
is a non-differentiable operation. This renders the first-order Monte Carlo
gradient estimator inapplicable and the learning feedback systematically
misleading. We propose a novel smoothing technique that creates a surrogate
market game, in which first-order methods can be applied. We provide
theoretical bounds on the resulting bias which justifies solving the smoothed
game instead. These bounds also allow choosing the smoothing strength a priori
such that the resulting estimate has low variance. Furthermore, we validate our
approach via numerous empirical experiments. Our method theoretically and
empirically outperforms zeroth-order methods in approximation quality and
computational efficiency.
