Efficient Imitation Learning and Life-long Policy Adaptation for Autonomous Vehicle Path Tracking

A life-long policy learning framework is proposed to efficiently learn and continuously improve an autonomous driving policy from imperfect demonstration data and incremental execution knowledge, achieving better path tracking accuracy and control smoothness compared to baseline methods.