Reward design has been one of the central challenges for real world reinforcement learning (RL) deployment, especially in settings with multiple objectives. Preference-based RL offers an appealing alternative by learning from human preferences over pairs of behavioural outcomes. More recently, RL from AI feedback (RLAIF) has demonstrated that large language models (LLMs) can generate preference labels at scale, mitigating the reliance on human annotators. However, existing RLAIF work typically focuses only on single-objective tasks, leaving the open question of how RLAIF handles systems that involve multiple objectives. In such systems trade-offs among conflicting objectives are difficult to specify, and policies risk collapsing into optimizing for a dominant goal. In this paper, we explore the extension of the RLAIF paradigm to multi-objective self-adaptive systems. We show that multi-objective RLAIF can produce policies that yield balanced trade-offs reflecting different user priorities without laborious reward engineering. We argue that integrating RLAIF into multi-objective RL offers a scalable path toward user-aligned policy learning in domains with inherently conflicting objectives.
An Efficient Deep Reinforcement Learning Model for Urban Traffic Control
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；Qingdao Academy of Intelligent Industries（青岛智能产业技术研究院）
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；School of Geosciences and Info-Physics, Central South University（中南大学地球科学与信息物理学院）
Traffic Scenario Logic: A Spatial-Temporal Logic for Modeling and Reasoning of Urban Traffic Scenarios
University of Science and Technology of China（中国科学技术大学）；Suzhou Shuzhi Technology Group Co., Ltd.（苏州数智科技集团有限公司）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
National Science Foundation（国家科学基金会）；U.S. Department of Transportation（美国交通部）；University Transportation Center (UTC)（大学交通中心）；Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)（交通网络安全高级研究与教育中心）；NVIDIA Academic Grant Program（NVIDIA学术资助计划）
