English(EN) SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations

SHRED框架使用自博弈在没有专家数据的情况下训练自主代理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了SHRED，一个新颖的框架，使自动驾驶汽车、行人和骑自行车的人能够通过自博弈学习复杂的交互，而无需依赖专家演示。该方法结合了特定类型的动态和目标与程序化场景生成和随机化，以培养多样化的行为。SHRED在Waymo Open Sim Agents Challenge (WOSAC) 和 InterPlan 基准测试中表现出色，优于无演示自博弈基线和使用演示训练策略的方法。研究强调，在自博弈中引入异构性可以显著提高交通的真实性和规划能力，尤其是在交互水平很高的场景中。 AI

影响展示了一种训练自主代理的新范例，有可能减少对大量人类演示数据集的依赖。

排序理由研究论文发表在arXiv上，详细介绍了用于自主代理的新AI框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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arXiv cs.AI TIER_1 English(EN) · Wei-Jer Chang, Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Masayoshi Tomizuka, Wei Zhan · 2026-10-16 04:00

SHRED：无需专家演示的异构道路使用者自博弈

arXiv:2610.20866v1 Announce Type: cross Abstract: Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints, and behaviors differ fundamentally. Existing approaches predominantly learn real…

本文如何被排名

报道来源 [1]

SHRED：无需专家演示的异构道路使用者自博弈

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