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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.LG2026
Dueling World Models: Advantage-Style Action Channels for Common-Mode Distractor Rejection
Jiazhuo Li, Yiming Fei, Zhiruo Zhou +1
Latent world models plan by predicting future states from an action, but when a scene contains motion the agent does not control, they quietly go action-blind: predictions for diff…