Nouran Adel Hassan
Papers
1
Total Citations
5
H-Index
1
About
Nouran Adel Hassan is a rising researcher in the fields of multi-robot systems, reinforcement learning, and intelligent control. Her work focuses on advancing decentralized coordination algorithms, particularly for flocking behaviors in multi-agent environments. Her most notable contribution is the development of a novel control framework using Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3), which addresses the overestimation bias inherent in the widely used Multi-Agent Deep Deterministic Policy Gradient (MADDPG). This work, published in 2022, has already garnered 5 citations, signaling its early impact on the robotics and AI communities. By improving the stability and performance of multi-robot flocking control, Hassan’s research offers practical solutions for applications in search-and-rescue, environmental monitoring, and autonomous swarms. Her approach bridges the gap between theoretical reinforcement learning and real-world robotic coordination, making her a promising voice in the next generation of multi-agent systems research.
Research Focus
Key Achievements
Top Papers
- 1