Adversarial behavioral cloning

Abstract

Imitation learning has been widely applied for autonomous robotics control. A popular IL approach is apprenticeship learning (AL) which alternates RL and inverse reinforcement learning (IRL). AL fundamentally requires a large number of environment interactions and thus takes a long time for training. We believe that IL algorithms would be more applicable to real-world problems if the number of interactions could be reduced as close to zero as possible. In this paper, we propose an IL algorithm which we call Adversarial Behavioral Cloning (ABC). Experimental results on MuJoCo physics simulator show that our algorithm achieves competitive results with a state-of-the-art AL algorithm, namely generative adversarial imitation learning (GAIL), even without any environment interactions.