Hoi-To Wai
Papers
1
Total Citations
14
H-Index
1
About
Hoi-To Wai is a researcher whose work sits at the intersection of distributed optimization, multi-agent systems, and reinforcement learning. His scholarship addresses some of the most pressing challenges in deploying intelligent systems across decentralized environments, including sensor networks, swarm robotics, and power grid management. One of his notable contributions, "Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization" (2018), tackles the inherent complexity of coordinating multiple learning agents — a problem that remains significantly more difficult than its single-agent counterpart. By developing principled optimization frameworks, Wai advances the theoretical foundations that enable agents to collaboratively evaluate policies without relying on centralized control. This work, which has garnered 14 citations, reflects a broader research agenda focused on making machine learning algorithms scalable, robust, and applicable to real-world networked systems. Wai's contributions are particularly valuable for students and practitioners working on federated learning, distributed signal processing, and cooperative AI, where the gap between theoretical guarantees and practical deployment remains a critical challenge. His research helps bridge that gap with rigorous, mathematically grounded methodologies.
Research Focus
Key Achievements
Top Papers
- 1