Xingang Mou
Papers
1
Total Citations
6
H-Index
1
About
Xingang Mou is a leading researcher in multirobot systems and intelligent decision-making, with a particular focus on pursuit-evasion dynamics and reinforcement learning. His most-cited work, "Multirobot Collaborative Pursuit Target Robot by Improved MADDPG" (2022), tackles the critical challenge of policy formulation in multirobot pursuit-evasion scenarios, where sparse rewards and unpredictable environmental changes hinder optimal strategy development. By enhancing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, Mou has advanced collaborative robotics, enabling more effective coordination among autonomous agents in complex, dynamic settings. His research addresses fundamental problems in multirobot systems, offering solutions that improve real-time adaptability and cooperation. With growing recognition in the field, Mou’s contributions are shaping the future of autonomous multiagent systems, making his work essential reading for researchers and students exploring reinforcement learning, swarm robotics, and intelligent control.
Research Focus
Key Achievements
Top Papers
- 1Multirobot Collaborative Pursuit Target Robot by Improved MADDPG6 citations · 2022