Papers
1
Total Citations
5
H-Index
1
About
Ning Yang is an emerging researcher specializing in multi-agent systems, swarm robotics, and reinforcement learning. Their work sits at the intersection of artificial intelligence and autonomous robotics, with a particular focus on developing adaptive, decentralized control strategies for cooperative robot swarms. Yang's most notable contribution, "Multi-Actor-Attention-Critic Reinforcement Learning for Central Place Foraging Swarms" (2021), addresses a fundamental challenge in swarm robotics: enabling multiple low-cost, decentralized agents to collaboratively complete foraging tasks more efficiently than single advanced robots. By leveraging attention-based critic mechanisms within a multi-agent reinforcement learning framework, Yang's approach moves beyond rigid, pre-designed algorithms toward truly adaptive swarm behavior — a meaningful step forward in making robot collectives more responsive to dynamic environments. This work has garnered 5 citations since its publication, reflecting growing interest in the research community. Yang's contributions are particularly relevant for researchers exploring scalable, robust solutions in autonomous systems, where decentralized coordination and emergent intelligence are increasingly critical. Their work lays valuable groundwork for future applications in search-and-rescue, environmental monitoring, and distributed logistics.
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Top Papers
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