Newest papers

Simulation Priors for Rapid Adaptation in Model-Based RL for Autonomous Racing

Adrian Remonda, Jiajun Xi, Nicklas Hansen, Marcus Greiff, Thomas Lew, John Talbot, John K. Subosits, Xiaolong Wang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26757 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

強化学習は、高性能コントロールポリシーを学習することを可能にしますが、広範な相互作用への依存は、データ収集が費用がかかる物理システムへの展開を制限し、失敗は危険です。我々は、非オンラインモデルベースのRLフレームワークであるPIAを導入し、構造化されたシミュレーションプリオールを使用して迅速な適応を可能にします。PIAは、異質な低信頼性と高信頼性のシミュレーション軌道で世界モデルをプレトレーニングし、その後、不確実性に基づく規制化された計画を使用して限られたターゲットドメイン相互作用でそれを適応します。政策転送のための正確なシミュレータを必要とするのではなく、PIA

Machine translation (Meta M2M100); the English original follows.

Abstract

Reinforcement learning enables learning high-performance control policies, but its reliance on extensive interaction limits deployment on physical systems where data collection is costly, and failures are unsafe. We introduce Prior-Informed Adaptation (PIA), an offline-to-online model-based RL framework that uses structured simulation priors for rapid adaptation. PIA pretrains a world model on heterogeneous low- and high-fidelity simulated trajectories, then adapts it with limited target-domain interaction using uncertainty-regularized planning. Rather than requiring an accurate simulator for policy transfer, PIA treats simulated trajectories as biased priors whose residual mismatch is corrected during online adaptation. We evaluate PIA in autonomous racing using high-fidelity simulation and on a real full-size autonomous race car. In simulation, PIA adapts faster across track and surface shifts than strong baselines, achieving lower lap times and fewer failures. On hardware, PIA achieves reliable few-shot adaptation with less than one hour of on-vehicle interaction.

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Topics: Prior probability, Reinforcement learning, Computer science, Adaptation (eye), Artificial intelligence, Software deployment

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