24 citations · 38 across the 9 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.MA2026
Offline Multi-Agent Reinforcement Learning with a Physics-Informed World Model for Cooperative Mixed Traffic Control
Lu Liu, Chi Xie, Xi Xiong
This study investigates cooperative control of connected and automated vehicles (CAVs) at partially observable highway bottlenecks in mixed traffic, aiming to mitigate congestion w…
cs.LG2026
Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Sample-efficient reinforcement learning for autonomous driving is often limited by the trade-off between data efficiency and model bias. While world models reduce the reliance on c…
cs.RO2026
Kinematics-Aware Latent World Models for Data-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Data-efficient learning remains a central challenge in autonomous driving due to the high cost and safety risks of large-scale real-world interaction. Although world-model-based re…