Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving
📰 ArXiv cs.AI
Fine-tuning is not enough for end-to-end autonomous driving, a parallel framework for collaborative imitation and reinforcement learning is proposed
Action Steps
- Identify the limitations of imitation learning in autonomous driving
- Incorporate reinforcement learning to improve performance
- Implement a parallel framework for collaborative imitation and reinforcement learning
- Evaluate the framework's performance and compare it to sequential fine-tuning
Who Needs to Know This
AI engineers and researchers working on autonomous driving systems can benefit from this framework as it improves the performance of end-to-end autonomous driving
Key Insight
💡 Sequential fine-tuning can introduce policy drift and lead to a performance ceiling, a parallel framework can overcome this limitation
Share This
🚗💻 Fine-tuning is not enough for autonomous driving! New parallel framework combines imitation & reinforcement learning #AI #AutonomousDriving
Key Takeaways
Fine-tuning is not enough for end-to-end autonomous driving, a parallel framework for collaborative imitation and reinforcement learning is proposed
Full Article
Title: Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving
Abstract:
arXiv:2603.13842v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving is typically built upon imitation learning (IL), yet its performance is constrained by the quality of human demonstrations. To overcome this limitation, recent methods incorporate reinforcement learning (RL) through sequential fine-tuning. However, such a paradigm remains suboptimal: sequential RL fine-tuning can introduce policy drift and often leads to a performance ceiling due to its dependence on the pret
Abstract:
arXiv:2603.13842v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving is typically built upon imitation learning (IL), yet its performance is constrained by the quality of human demonstrations. To overcome this limitation, recent methods incorporate reinforcement learning (RL) through sequential fine-tuning. However, such a paradigm remains suboptimal: sequential RL fine-tuning can introduce policy drift and often leads to a performance ceiling due to its dependence on the pret
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