Kang Hao Cheong
Papers
2
Total Citations
33
H-Index
2
About
Kang Hao Cheong is a leading researcher at the intersection of artificial intelligence, swarm robotics, and computational neuroscience. His work focuses on developing advanced decision-making frameworks for multiagent systems, particularly through reinforcement learning and bio-inspired optimization algorithms. Cheong’s most notable contribution is the expert system-based multiagent deep deterministic policy gradient (ESB-MADDPG), a novel approach that enhances swarm robot coordination by integrating expert knowledge with deep reinforcement learning. This work, published in 2022 and garnering 31 citations, addresses critical challenges in decentralized decision-making for robotic collectives. Additionally, Cheong has explored multi-objective squirrel search algorithms for EEG feature selection, demonstrating the versatility of his optimization techniques in biomedical signal processing. His research bridges theoretical algorithm design with practical applications in robotics and neural engineering, offering scalable solutions for complex, real-time environments. Cheong’s work is particularly impactful for students and researchers interested in multiagent systems, reinforcement learning, and the integration of AI with biological signal analysis, as it provides both foundational insights and actionable methodologies for advancing autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Multi-objective squirrel search algorithm for EEG feature selection2 citations · 2023