Anqi Wang
Papers
1
Total Citations
58
H-Index
1
About
Anqi Wang is a leading researcher in intelligent robotics and multi-agent systems, with a primary focus on reinforcement learning for dynamic task allocation in logistics and warehouse automation. Wang’s most influential work, “A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation” (2021), has garnered 58 citations and introduces a groundbreaking hierarchical framework that addresses the complex scheduling challenges of autonomous guided vehicles (AGVs) in goods-to-man systems. By integrating the Soft Actor-Critic algorithm with a hierarchical structure, Wang’s method significantly enhances the efficiency and adaptability of multi-robot coordination in real-time, dynamic environments. This contribution is pivotal for advancing intelligent unmanned warehouses, where order variability and robot performance directly impact operational throughput. Wang’s research bridges the gap between theoretical reinforcement learning and practical industrial applications, offering scalable solutions for logistics automation. Recognized for this work, Wang continues to shape the future of autonomous systems, inspiring further innovations in multi-agent task allocation and smart manufacturing.
Research Focus
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Top Papers
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