arXiv cs.AI· Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani·· 6 天前AI 评分20
偏好条件多目标强化学习用于重型车辆战术决策
Preference-Conditioned Multi-Objective Reinforcement Learning for Tactical Decision Making in Heavy-Duty Vehicles
AI 导读
研究提出一种偏好条件多目标强化学习框架，结合广义策略改进与PPO，为重型卡车在安全、能耗与时间效率三个冲突目标间学习不同权衡策略。该方法在可扩展的高速公路交通仿真中生成平滑可解释的Pareto前沿，跨交通密度及对比解析基线的评估显示策略捕捉到有意义的权衡，且无需重新训练即可切换驾驶行为。
正文 · 原文
Abstract:A central difficulty in applying reinforcement learning to real-world applications such as autonomous driving is that conventional scalar reward formulations obtained by aggregating multiple often competing objectives may obscure the structure of their trade-offs. These challenges are particularly relevant to decision-making for heavy-duty vehicles, which requires balancing safety, efficiency, and operational costs under demanding commercial requirements. We present a preference-conditioned multi-objective reinforcement learning framework that explicitly represents these objectives and utilizes Generalized Policy Improvement and Proximal Policy Optimization to learn policies capturing different trade-offs between them. The proposed method approximates the Pareto front across three conflicting objectives: safety, quantified in terms of collisions and successful completion; energy efficiency, quantified using energy cost; and time efficiency, quantified using driver cost. We evaluate the framework in a scalable highway traffic simulation environment tailored for tactical decision making in heavy-duty trucks. It yields smooth and interpretable Pareto fronts, and evaluations across traffic densities and against an analytical baseline show that the learned policies capture meaningful trade-offs. The preference-conditioned policy enables transitions between different driving behaviors without retraining, demonstrating the potential of multi-objective reinforcement learning for adaptive decision making in autonomous heavy-duty vehicles.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2601.18783 [cs.LG] |
| (or arXiv:2601.18783v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2601.18783 arXiv-issued DOI via DataCite |
Submission history
From: Deepthi Pathare [view email]
[v1]
Mon, 26 Jan 2026 18:50:21 UTC (360 KB)
[v2]
Mon, 1 Jun 2026 08:59:51 UTC (437 KB)
[v3]
Fri, 9 Oct 2026 13:22:43 UTC (459 KB)
来源：arXiv cs.AI · arxiv.org