We address the problem of solving complex Bayesian games, characterized by high-dimensional type and action spaces, many (> 2) players, general-sum payoffs. Our approach applies to symmetric one-shot with no given analytic structure. represent agent strategies in parametric form as neural networks, apply natural evolution (NES) [wierstra2014natural] for deep model optimization. For pure equi...