Pengcheng Dai
Papers
2
Total Citations
49
H-Index
2
About
Pengcheng Dai is a leading researcher in multi-agent reinforcement learning (MARL), with a focus on developing scalable and theoretically grounded algorithms for cooperative and networked systems. His major contributions include the introduction of the DTDE (Distributed Training, Decentralized Execution) framework, a novel cooperative MARL paradigm that addresses key challenges in agent coordination and policy learning. This work, published in 2021, has already garnered 44 citations, reflecting its significant impact on the field. More recently, Dai has advanced the state of the art with a distributed neural policy gradient algorithm that ensures global convergence in networked multi-agent settings—a critical step beyond linear function approximation methods. His research bridges the gap between practical multi-agent systems and rigorous theoretical guarantees, offering new pathways for applications in robotics, autonomous driving, and distributed control. By tackling both the expressiveness and convergence challenges in MARL, Pengcheng Dai is shaping the future of intelligent, cooperative decision-making in complex environments.
Research Focus
Key Achievements
Top Papers
- 1DTDE: A new cooperative multi-agent reinforcement learning framework44 citations · 2021
- 2