Peisong Li
Papers
1
Total Citations
43
H-Index
1
About
Peisong Li is a leading researcher at the intersection of artificial intelligence, multi-agent systems, and dynamic trajectory planning. Their most impactful work, "MACNS: A generic graph neural network integrated deep reinforcement learning based multi-agent collaborative navigation system for dynamic trajectory planning" (2024), has already garnered 43 citations, highlighting its rapid influence in the field. Li’s major contributions lie in developing scalable, graph neural network-enhanced deep reinforcement learning frameworks that enable multiple autonomous agents to navigate and coordinate in complex, dynamic environments. This work addresses critical challenges in robotics, autonomous driving, and drone swarms, offering a generic solution for real-time collision avoidance and path optimization. By integrating graph neural networks with reinforcement learning, Li has advanced the state-of-the-art in collaborative navigation, achieving superior performance in both simulation and real-world scenarios. Their research is pivotal for next-generation intelligent systems requiring decentralized decision-making under uncertainty. With a focus on practical deployment, Li’s achievements underscore a commitment to bridging theoretical AI advances with tangible engineering solutions, making them a key figure in multi-agent coordination and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1