We study the TAPF (combined target-assignment and path-finding) problem for teams of agents in known terrain, which generalizes both the anonymous and non-anonymous multi-agent path-finding problems. Each of the teams is given the same number of targets as there are agents in the team. Each agent has to move to exactly one target given to its team such that all targets are visited. The TAPF problem is to first assign agents to targets and then plan collision-free paths for the agents to their targets in a way such that the makespan is minimized. We present the CBM (Conflict-Based Min-Cost-Flow) algorithm, a hierarchical algorithm that solves TAPF instances optimally by combining ideas from anonymous and non-anonymous multi-agent path-finding algorithms. On the low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths. On the high level, CBM uses conflict-based search to resolve collisions among agents in different teams. Theoretically, we prove that CBM is correct, complete and optimal. Experimentally, we show the scalability of CBM to TAPF instances with dozens of teams and hundreds of agents and adapt it to a simulated warehouse system.
A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem
Beijing Institute of Aerospace Systems Engineering（北京宇航系统工程研究所）；Beijing Aerospace Automatic Control Institute（北京航天自动控制研究所）；China Academy of Launch Vehicle Technology（中国运载火箭技术研究院）
RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective
Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳））；School of Mechanical Engineering and Automation（机械工程与自动化学院）；Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
Area Protection in Adversarial Path-Finding Scenarios with Multiple Mobile Agents on Graphs: a theoretical and experimental study of target-allocation strategies for defense coordination
University of Bergen（卑尔根大学）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）
EvoSteer: Online Self-Evolving Graph Orchestration via Reference-Anchored Credit Assignment
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Dalian University of Technology（大连理工大学）；Fudan University（复旦大学）；University of Oxford（牛津大学）；North China Electric Power University（华北电力大学）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
