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Lin-Jiang Cao

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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