Yuezhang Li

Papers

Total Citations

107

H-Index

About

Yuezhang Li is a leading researcher in artificial intelligence, with a primary focus on deep reinforcement learning and computer vision. His most influential work, "Object-sensitive Deep Reinforcement Learning" (2018), has garnered 107 citations and addresses a critical gap in the field: the underutilization of object-level information in reinforcement learning models. By integrating object characteristics into deep learning frameworks, Li demonstrated significant improvements in visual-input tasks, including Atari game playing and robot navigation, where traditional pixel-based approaches often fall short. This contribution has advanced the understanding of how agents can perceive and interact with their environments more intelligently. Li’s research bridges the gap between object detection and decision-making, offering a pathway toward more robust and interpretable AI systems. His work is particularly notable for its practical implications in robotics and autonomous systems, where object awareness is crucial for real-world performance. With a growing citation impact, Yuezhang Li continues to shape the future of intelligent agents, inspiring new directions in object-centric reinforcement learning.