Chenyu Zhao
Papers
1
Total Citations
2
H-Index
1
About
Chenyu Zhao is an emerging researcher in the field of multiagent systems and reinforcement learning, with a focused interest in autonomous coordination and control in complex environments. Their work addresses the challenging problem of multiagent encirclement control, particularly in environments cluttered with obstacles. Zhao’s most notable contribution is a policy-guided reinforcement learning method designed to enable multiple agents to collaboratively surround a mobile target while simultaneously avoiding collisions with obstacles—a problem known as EMOCA (Encirclement with Multi-Obstacle Collision Avoidance). This work, published in 2025, has already garnered attention with 2 citations, signaling its relevance to researchers working on autonomous swarm robotics and intelligent control systems. By developing a framework that balances the tradeoff between target pursuit and obstacle avoidance, Zhao has provided a practical solution for real-world applications such as surveillance, search-and-rescue, and autonomous navigation. Their research stands out for its novel integration of reinforcement learning with policy guidance, offering a scalable and adaptive approach to multiagent coordination. As an early-career researcher, Chenyu Zhao is poised to make further contributions to the advancement of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1