English(EN) Human-Feedback-Guided Multi-Agent Inverse Reinforcement Learning for Interactive Driving Behavior Modeling

新框架利用人类反馈模拟交互式驾驶行为

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一个名为“基于人类反馈的多智能体逆强化学习”（HFG-MAIRL）的新框架，以更好地模拟混合交通场景中的人类驾驶行为。该框架通过显式地在局部马尔可夫博弈中模拟车辆之间的交互，而不是将其他车辆视为单纯的障碍物，从而解决了现有方法的局限性。它还纳入了人类反馈，如驾驶员情绪标签，作为序数偏好来改进奖励恢复。在AIDE数据集上的实验表明，HFG-MAIRL在决策建模方面优于传统的逆强化学习基线，并生成了可解释的、与反馈一致的奖励模式。 AI

影响这项研究可能导致更复杂的自动驾驶系统，能够驾驭复杂、交互式的交通环境。

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在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Longhao Yan, Jinhao Liang, Kaidi Yang · 2026-10-27 04:00

面向交互式驾驶行为建模的基于人类反馈引导的多智能体逆强化学习

arXiv:2610.33726v1 Announce Type: new Abstract: Modeling human driving behavior is fundamental to understanding driver decision-making and supporting autonomous vehicles (AVs) in mixed traffic. Reward learning offers a principled way to infer the latent objectives behind driving …

本文如何被排名

报道来源 [1]

面向交互式驾驶行为建模的基于人类反馈引导的多智能体逆强化学习

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