CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in
  Latent World Models for Autonomous Driving
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.
68ef1c02486b78128f0e348c
Xiaoji Zheng
claimed_verified
2025-10-15T07:08:32.045Z
655601f1ae085c2ba7a22b95
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/4UmxFrc_TEiXcnm3RewZM.jpeg
Xiaoji Zheng
user
Student-Xiaoji
68ef1c02486b78128f0e348d
Ziyuan Yang
68ef1c02486b78128f0e348e
Yanhao Chen
68ef1c02486b78128f0e348f
Yuhang Peng
68ef1c02486b78128f0e3490
Yuanrong Tang
68ef1c02486b78128f0e3491
Gengyuan Liu
68ef1c02486b78128f0e3492
Bokui Chen
68ef1c02486b78128f0e3493
Jiangtao Gong
CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in
  Latent World Models for Autonomous Driving
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.