Before revision: This paper develops estimation and inference tools for the structural parameter in the dynamic game with a high-dimensional state space under the assumption that the data are generated by a single Markov perfect equilibrium. The equilibrium assumption implies that the expected value function evaluated at the equilibrium strategy profile is not smaller than the expected value function evaluated at a feasible suboptimal alternative. The target identified set is defined as the set of parameters obeying this inequality restriction. I estimate the expected value function in the two-stage procedure. At the first stage, I estimate the  law of motion of the state variable and the equilibrium policy function using modern machine learning methods.
After revision: This paper develops estimation and inference tools for the structural parameter in the dynamic game with a high-dimensional state space into the dynamic model of imperfect competition. In the first stage, I estimate the law of motion of the state variable and the equilibrium policy function using modern machine learning methods.