Sampling-based model predictive control (MPC) has found significant success in optimal control problems with non-smooth system dynamics and cost function. Many machine learning-based works proposed to improve MPC by a) learning or fine-tuning the dynamics/ cost function, or b) learning to optimize for the update of the MPC controllers. For the latter, imitation learning-based optimizers are trained to update the MPC controller by mimicking the expert demonstrations, which, however, are expensive or even unavailable. More significantly, many sequential decision-making problems are in non-stationary environments, requiring that an optimizer should be adaptable and generalizable to update the MPC controller for solving different tasks. To address those issues, we propose to learn an optimizer based on meta-reinforcement learning (RL) to update the controllers. This optimizer does not need expert demonstration and can enable fast adaptation (e.g., few-shots) when it is deployed in unseen control tasks. Experimental results validate the effectiveness of the learned optimizer regarding fast adaptation.
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
ETH Zurich（苏黎世联邦理工学院）；The Chinese University of Hong Kong（香港中文大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Centre for Perceptual and Interactive Intelligence（感知与交互智能研究中心）
Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems
King’s College London（伦敦国王学院）；Eindhoven University of Technology（埃因霍温理工大学）；University of California, San Diego（加州大学圣地亚哥分校）；The Alan Turing Institute（艾伦·图灵研究所）
Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；OPPO Guangdong Mobile Communications Co., Ltd.（OPPO广东移动通信有限公司）
Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning
College of Electronic Engineering, National University of Defense Technology（国防科技大学电子工程学院）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学北京信息科学与技术国家研究中心）；College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control
Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Cisco-NUS Accelerated Digital Economy Corporate Laboratory（Cisco-NUS加速数字经济企业实验室）
MetaToolAgent: Towards Generalizable Tool Usage in LLMs through Meta-Learning
College of Informatics, Huazhong Agricultural University（华中农业大学信息学院）；Industrial AI Research Centre, University of South Australia（南澳大利亚大学工业AI研究中心）；School of Computer Science, Wuhan University（武汉大学计算机学院）
MetaFollower: Adaptable Personalized Autonomous Car Following
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Tongji University（同济大学）；University of Washington（华盛顿大学）
Generalizable Black-Box Adversarial Attack with Meta Learning
Tsinghua University（清华大学）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Tencent AI Lab（腾讯人工智能实验室）；University of Electronic Science and Technology of China（电子科技大学）
A Dynamic Time Warping-Transfer Learning Approach to Transferring Knowledge in Stress-strain Behaviors from Polymers to Metals: An Affordable and Generalizable Additive Manufacturing Part Qualification Framework
Department of Mechanical and Aerospace Engineering, College of Engineering and Computer Science, University of Central Florida（机械与航空航天工程系，工程与计算机科学学院，中央佛罗里达大学）
Generalizable Heuristic Generation Through LLMs with Meta-Optimization
Nanyang Technological University（南洋理工大学）；Shandong University（山东大学）；Singapore Management University（新加坡国立大学）；Eindhoven University of Technology（埃因霍温理工大学）
Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)
ICTFicial；Oulu University（奥卢大学）；Ruhr University Bochum（波鸿鲁尔大学）；Southeast University（东南大学）；Seoul National University（首尔大学）；Sejong University（世宗大学）；Futurewei Technologies（未来科技公司（Futurewei Technologies））
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
Nanyang Technological University（南洋理工大学）；Eindhoven University of Technology（埃因霍温理工大学）；Shandong University（山东大学）；Singapore Management University（新加坡管理大学）
