CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in
  Latent World Models for Autonomous Driving
Authors: name: Xiaoji Zheng; name: Ziyuan Yang; name: Yanhao Chen; name: Yuhang Peng; name: Yuanrong Tang; name: Gengyuan Liu; name: Bokui Chen; name: Jiangtao Gong
Published: 2025-10-14 14:21:52

Abstract: End-to-end autonomous driving models trained solely with imitation learning
(IL) often suffer from poor generalization. In contrast, reinforcement learning
(RL) promotes exploration through reward maximization but faces challenges such
as sample inefficiency and unstable convergence. A natural solution is to
combine IL and RL. Moving beyond the conventional two-stage paradigm (IL
pretraining followed by RL fine-tuning), we propose CoIRL-AD, a competitive
dual-policy framework that enables IL and RL agents to interact during
training. CoIRL-AD introduces a competition-based mechanism that facilitates
knowledge exchange while preventing gradient conflicts. Experiments on the
nuScenes dataset show an 18% reduction in collision rate compared to baselines,
along with stronger generalization and improved performance on long-tail
scenarios. Code is available at: https://github.com/SEU-zxj/CoIRL-AD.