A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
University College London（伦敦大学学院）；Huawei R&D U.K.（华为英国研发部）；Huawei Canada（华为加拿大公司）；Shanghai Jiao Tong University（上海交通大学）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Tianjin University（天津大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Northwestern Polytechnical University（西北工业大学）
Bidirectional Curriculum Generation: A Multi-Agent Framework for Data-Efficient Mathematical Reasoning
Zhejiang University（浙江大学）；University of Macau（澳门大学）；Peking University（北京大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Wuhan University（武汉大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；School of School Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；College of Civil Engineering, Tongji University（同济大学土木学院）
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs
National University of Singapore（新加坡国立大学）；HeartVoice Medical Technology（心声医疗科技）；University of Science and Technology of China（中国科学技术大学）；Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院附属邵逸夫医院）；Peking University（北京大学）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）
