InDRiVE: Reward-Free World-Model Pretraining for Autonomous Driving via Latent Disagreement

• URL: http://arxiv.org/abs/2512.18850v2
• Date: Sat, 27 Dec 2025 05:30:36 GMT
• Title: InDRiVE: Reward-Free World-Model Pretraining for Autonomous Driving via Latent Disagreement
• Abstract summary: This paper presents InDRiVE, a DreamerV3-style model-based reinforcement learning agent that performs reward-free pretraining in CARLA.<n>Disagreement-based pretraining yields stronger zero-shot and robust few-shot collision avoidance under town shift and matched interaction budgets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based reinforcement learning (MBRL) can reduce interaction cost for autonomous driving by learning a predictive world model, but it typically still depends on task-specific rewards that are difficult to design and often brittle under distribution shift. This paper presents InDRiVE, a DreamerV3-style MBRL agent that performs reward-free pretraining in CARLA using only intrinsic motivation derived from latent ensemble disagreement. Disagreement acts as a proxy for epistemic uncertainty and drives the agent toward under-explored driving situations, while an imagination-based actor-critic learns a planner-free exploration policy directly from the learned world model. After intrinsic pretraining, we evaluate zero-shot transfer by freezing all parameters and deploying the pretrained exploration policy in unseen towns and routes. We then study few-shot adaptation by training a task policy with limited extrinsic feedback for downstream objectives (lane following and collision avoidance). Experiments in CARLA across towns, routes, and traffic densities show that disagreement-based pretraining yields stronger zero-shot robustness and robust few-shot collision avoidance under town shift and matched interaction budgets, supporting the use of intrinsic disagreement as a practical reward-free pretraining signal for reusable driving world models.

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