Minjie Zhang
Papers
6
Total Citations
71
H-Index
3
About
Dr. Minjie Zhang is a leading researcher in multiagent systems, with a particular focus on multiagent learning, coordination, and task allocation. Her most cited work, "Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems" (2015, 59 citations), addresses the fundamental challenge of nonstationarity in concurrent multiagent learning environments, proposing efficient algorithms for agents to learn coordinated behaviors. This contribution has been influential in advancing the theoretical foundations of multiagent reinforcement learning. Dr. Zhang also applies her expertise to critical real-world problems, notably in disaster response robotics. She has developed innovative approaches for deploying wireless mobile robots to establish ad hoc communication networks in disaster environments, maximizing coverage of important locations (2015) and enabling efficient search and deployment (2014). Her recent work extends into multiagent task allocation and planning with multi-objective requirements using Linear Temporal Logic (2021), and real-time path planning from image information (2021). With a research portfolio spanning from foundational multiagent learning theory to practical robotic deployment in emergencies, Dr. Zhang demonstrates a unique ability to bridge theoretical advances with impactful applications in disaster management and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems59 citations · 2015
- 2
- 3
- 4
- 5
- 6