Author

Jiazhuo Li

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Preprint Aug 2026

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving

The Dreamer-SAC framework, which integrates a recurrent state-space world model with an off-policy soft actor-critic algorithm trained directly in latent space, uses a combination of real interactions and short-horizon generated trajectories with n-step target estimation and multi-objective supervision.

Jiazhuo Li, Lin-Jiang Cao, Qi Liu et al. · 0 citations

Preprint Aug 2026

Dueling World Models: Advantage-Style Action Channels for Common-Mode Distractor Rejection

In latent dynamics, subtracting a prediction's mean effect over actions cancels whatever the actions share--the action-independent variation where distractors live--leaving a clean, controllable channel, with no reward, no reconstruction, and no distractor-specific auxiliary loss.

Jiazhuo Li, Yiming Fei, Zhiruo Zhou et al. · 0 citations