Reinforcement learning in heterogeneous multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in homogeneous settings and simple benchmarks. In this work, we present an actor-critic algorithm that allows a team of heterogeneous agents to learn decentralized control policies for covering an unknown environment. This task is of interest to national security and emergency response organizations that would like to enhance situational awareness in hazardous areas by deploying teams of unmanned aerial vehicles. To solve this multi-agent coverage path planning problem in unknown environments, we augment a multi-agent actor-critic architecture with a new state encoding structure and triplet learning loss to support heterogeneous agent learning. We developed a simulation environment that includes real-world environmental factors such as turbulence, delayed communication, and agent loss, to train teams of agents as well as probe their robustness and flexibility to such disturbances.
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space
School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University（北京交通大学交通数据分析与挖掘北京市重点实验室）
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；University of Oxford（牛津大学）；University College London（伦敦大学学院）；ReLER, AAII, University of Technology Sydney（悉尼科技大学AAII ReLER实验室）
School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University（北京交通大学交通数据分析与挖掘北京市重点实验室）
Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
Department of Electrical and Computer Engineering, Royal Military College of Canada（加拿大皇家军事学院电子与计算机工程系）；Defence Research and Development Canada（加拿大国防研究与发展加拿大）；Department of Computer Engineering and Software Engineering, Polytechnique Montréal（蒙特利尔理工学院计算机工程与软件工程系）
Multi-Agent Reinforcement Learning for Heterogeneous Satellite Cluster Resources Optimization
STEM, University of South Australia（南澳大利亚大学STEM学院）；School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), Curtin University（Curtin大学电气工程、计算与数学科学学院）；Systems Research Institute,Polish Academy of Sciences（波兰科学院系统研究所）
MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion
Faculty of Robot Science and Engineering, Northeastern University（机器人科学与工程学院，东北大学）；Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics and School of Intelligence Science and Engineering, the Harbin Institute of Technology Shenzhen（智能变形机制与适应机器人重点实验室和智能科学与工程学院，哈尔滨工业大学深圳）
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）；Purple Mountain Laboratories（紫金山实验室）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Wireless Technology Lab, 2012 Laboratories, Huawei Technologies Company Ltd.（华为技术有限公司无线技术实验室）；ITP Lab, the Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电子与电气工程系ITP实验室）
Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning
Department of Mechanical and Aerospace Engineering, George Washington University（机械与航空航天工程系，乔治华盛顿大学）；Department of Electrical and Computer Engineering, Purdue University（电气与计算机工程系，普渡大学）
Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
Institute for AI, Peking University（北京大学人工智能研究院）；National Key Laboratory of General AI, BIGAI（北京智源人工智能研究院通用人工智能全国重点实验室）；University of Technology Sydney（悉尼科技大学）；Tencent AI Lab（腾讯人工智能实验室）
