Xi Gui
Papers
1
Total Citations
8
H-Index
1
About
Xi Gui is a leading researcher in multi-agent systems and cooperative robotics, with a focus on applying deep reinforcement learning to solve complex, real-world control challenges. His most-cited work, "Deep Reinforcement Learning of Cooperative Control with Four Robotic Agents by MADDPG" (2020, 8 citations), addresses the limitations of classical algorithms—such as inflexibility and non-robustness—by introducing a multi-agent deep deterministic policy gradient (MADDPG) framework. This contribution demonstrates how agents can learn cooperative behaviors without requiring a full global view, advancing the field toward more scalable and adaptive robotic teams. Gui’s research bridges the gap between theoretical reinforcement learning and practical multi-robot coordination, offering solutions for applications in autonomous exploration, swarm robotics, and distributed control. His work is particularly valued for its emphasis on real-world feasibility, tackling the inherent complexity and uncertainty of multi-agent environments. With a growing citation footprint, Xi Gui continues to shape how intelligent systems collaborate, making his research essential reading for students and engineers working at the intersection of AI, robotics, and autonomous systems.
Research Focus
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Top Papers
- 1