PlannerRFT: 通过闭环和样本高效微调强化扩散规划器
PlannerRFT: Reinforcing Diffusion Planners through Closed-Loop and Sample-Efficient Fine-Tuning
AI总结：
PlannerRFT通过闭环和样本高效微调提升扩散规划器的多模态轨迹生成能力，实现高效探索与鲁棒性增强。
摘要：
基于扩散的规划器在自动驾驶中的人类轨迹生成中已成为有前途的方法。最近的工作通过生成-评估循环中的奖励导向优化来增强扩散规划器的鲁棒性。然而，它们难以生成多模态、场景自适应的轨迹，阻碍了在微调过程中利用信息性奖励的效率。为了解决这个问题，我们提出了PlannerRFT，一种针对扩散规划器的样本高效强化微调框架。PlannerRFT采用双分支优化，同时细化轨迹分布并自适应引导去噪过程向更有前途的探索方向发展，而无需修改原始推断流程。为了支持大规模并行学习，我们开发了nuMax，一个优化的模拟器，其滚动速度比原生nuPlan快10倍。大量实验表明，PlannerRFT在学习过程中表现出独特的行为，取得了最先进的性能。
英文摘要：
Diffusion-based planners have emerged as a promising approach for human-like trajectory generation in autonomous driving. Recent works incorporate reinforcement fine-tuning to enhance the robustness of diffusion planners through reward-oriented optimization in a generation-evaluation loop. However, they struggle to generate multi-modal, scenario-adaptive trajectories, hindering the exploitation efficiency of informative rewards during fine-tuning. To resolve this, we propose PlannerRFT, a sample-efficient reinforcement fine-tuning framework for diffusion-based planners. PlannerRFT adopts a dual-branch optimization that simultaneously refines the trajectory distribution and adaptively guides the denoising process toward more promising exploration, without altering the original inference pipeline. To support parallel learning at scale, we develop nuMax, an optimized simulator that achieves 10 times faster rollout compared to native nuPlan. Extensive experiments shows that PlannerRFT yields state-of-the-art performance with distinct behaviors emerging during the learning process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
