Problem
Research questions and friction points this paper is trying to address.
Cooperative Control
Mixed Traffic
Highway Bottlenecks
Offline Reinforcement Learning
Physics-Informed World Model
Offline Multi-Agent Reinforcement Learning with a Physics-Informed World Model for Cooperative Mixed Traffic Control
🤖 AI Summary
研究通过物理信息世界模型和离线多智能体强化学习，解决混合交通中部分可见瓶颈的拥堵问题，不依赖全局交通状态或在线试错。
📝 Abstract
This study investigates cooperative control of connected and automated vehicles (CAVs) at partially observable highway bottlenecks in mixed traffic, aiming to mitigate congestion without relying on complete global traffic states or online trial-and-error. We propose a physics-informed world model-based offline multi-agent reinforcement learning framework that reconstructs a physically interpretable global traffic state from local CAV observation-action histories, with coupled macroscopic-microscopic traffic dynamics providing physics-based supervision. A probabilistic ensemble world model learns traffic-state transitions and system rewards, while model disagreement quantifies epistemic uncertainty. Multi-step imagined rollouts with pessimistic rewards and uncertainty-driven truncation are then used for offline policy learning. Experiments in a SUMO-based on-ramp bottleneck using approximately $1\times10^6$ offline transitions show that physics supervision improves state reconstruction and world-model prediction accuracy.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-informed world model
Offline multi-agent reinforcement learning
Mixed traffic control
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