Imitation Learning with Approximated Behavior Cloning Loss
Corey A. Lowman,Joshua S. McClellan,Galen E. Mullins,Corey A. Lowman,Joshua S. McClellan,Galen E. Mullins
Recent Imitation Learning (IL) techniques focus on adversarial imitation learning algorithms to learn from a fixed set of expert demonstrations. While these approaches are theoretically sound, they suffer from a number of problems such as poor sample efficiency, poor stability, and a host of issues that Generative Adversarial Networks (GANs) suffer from. In this paper we introduce a generalization...
