MixGAIL: Autonomous Driving Using Demonstrations with Mixed Qualities
Gunmin Lee, Dohyeong Kim, Wooseok Oh, Kyungjae Lee, Songhwai Oh
Abstract
In this paper, we consider autonomous driving of a vehicle using imitation learning. Generative adversarial imitation learning (GAIL) is a widely used algorithm for imitation learning. This algorithm leverages positive demonstrations to imitate the behavior of an expert. In this paper, we propose a novel method, called mixed generative adversarial imitation learning (MixGAIL), which incorporates both of expert demonstrations and negative demonstrations, such as vehicle collisions. To this end, the proposed method utilizes an occupancy measure and a constraint function. The occupancy measure is used to follow expert demonstrations and provides a positive feedback. On the other hand, the constraint function is used for negative demonstrations to assert a negative feedback. Experimental results show that the proposed algorithm converges faster than the other baseline methods. Also, hardware experiments using a real-world RC car shows an outstanding performance and faster convergence compared with existing methods.