Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving
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 costly environment interactions, policy optimization over learned dynamics remains sensitive to prediction errors. This paper proposes 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. The framework uses a combination of real interactions and short-horizon generated trajectories with n-step target estimation and multi-objective supervision. Evaluated in autonomous driving scenarios with objectives encomp...
| Authors | Jiazhuo Li, Linjiang Cao, Qi Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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