PRIMAL3让上万个智能体高效避障，靠学习与模仿结合实现城市级路径规划。
PRIMAL3: Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Leveraging LaCAM3
- 用强化学习+模仿学习，让多智能体在关键瓶颈处自主协调
- 支持10万智能体规模，比现有方法成功率高且不撞车
- 适合机器人集群、交通调度等大规模协同场景
我们提出PRIMAL3，一种超大规模多智能体路径规划学习框架，融合强化学习、拓扑感知通信、LaCAM3引导训练及基于PIBT的动作优化。该框架聚焦于拓扑关键状态下的失败问题，如瓶颈、死胡同和持续冲突，每个智能体通过割点、死胡同区域、最短路径距离和阻塞估计等特征表示。构建两个互补图：同向跟随图传递沿兼容路径的多跳上下文，异向冲突图通过掩码注意力和相对特征区分竞争共享空间的智能体。训练中，通过策略熵识别不确定智能体，由LaCAM3提供置信度触发的动作干预和标签平滑的模仿目标。推理时，优先级感知的PIBT模块结合学习到的距离感知优先级与策略自适应回退偏好，对联合动作进行精修，保持无碰撞执行。该框架在无需推理阶段使用LaCAM3的情况下，实现了学习探索与结构化专家指导的结合。实验表明，PRIMAL3显著优于现有学习型基线，可扩展至最多10万智能体的城市级规模。真实世界实验验证了其在物理机器人系统上的可行性，消融研究也证实了各组件的有效性。
原文摘要 · Abstract (English)
We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aware communication, LaCAM3-guided training, and PIBT-based action refinement. PRIMAL3 targets failures at topologically critical states, where agents must coordinate decisively around bottlenecks, dead ends, and persistent conflicts. Each agent is represented using features derived from cut vertices, dead-end regions, shortest-path distances, and blocking estimates. Two complementary graphs capture agent interactions: a same-direction following graph propagates multihop context along compatible paths, while a different-direction conflict graph differentiates agents competing for shared space through masked attention and relative features. During training, we propose to let policy entropy identify uncertain agents, for which LaCAM3 provides confidence-triggered action interventions and label-smoothed imitation targets. During execution, a priority-aware PIBT module refines the proposed joint actions using persistent, learned, and distance-aware priorities together with policy-aware fallback preferences while maintaining collision-free execution. The resulting framework combines learned exploration with structured expert guidance without requiring LaCAM3 at inference. Experiments demonstrate that PRIMAL3 substantially outperforms state-of-the-art learning-based baselines and scales to ultra-large instances with up to city-level 100,000 agents. Real-world experiments further demonstrate the feasibility of deploying PRIMAL3 on physical robotic systems and ablation studies validate the individual contributions the components we proposed. Project page: https://marmotlab.github.io/PRIMAL3/
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