Generalization and Computation for Policy Classes of Generative Adversarial Imitation Learning

Generative adversarial imitation learning (GAIL) learns an optimal policy by expert demonstrations from the environment with unknown reward functions. Different from existing works that studied the generalization of reward function classes or discriminator classes, we focus on policy classes. This paper investigates the generalization and computation for policy classes of GAIL. Specifically, our contributions lie in: 1) We prove that the generalization is guaranteed in GAIL when the complexity of policy classes is properly controlled. 2) We provide an off-policy framework called the two-stage stochastic gradient (TSSG), which can efficiently solve GAIL based on the soft policy iteration and attain the sublinear convergence rate to a stationary solution. The comprehensive numerical simulations are illustrated in MuJoCo environments.