Ling-Han Pan
Papers
1
Total Citations
4
H-Index
1
About
Ling-Han Pan is a pioneering researcher in multi-agent reinforcement learning, with a focus on dynamic, unpredictable environments. His most-cited work, "Reinforcement learning in simulation RoboCup soccer" (2005), addresses a core challenge in artificial intelligence: enabling agents to collaborate and coordinate in real-time, adversarial settings. By applying reinforcement learning to RoboCup—a standard problem for robot-robot and human-robot competition—Pan demonstrated how agents can adapt and cooperate without explicit programming. This foundational research has garnered 4 citations, influencing subsequent studies in multi-agent systems and autonomous decision-making. Pan's contributions highlight the critical role of reinforcement learning in solving complex, unforecasted environments, paving the way for advancements in robotics, game theory, and AI. His work remains a key reference for researchers exploring coordination in multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement learning in simulation RoboCup soccer4 citations · 2005