Papers
1
Total Citations
11
H-Index
1
About
Huanyu Zang’s research lies at the intersection of reinforcement learning, decision-making under uncertainty, and intelligent systems. Their most impactful work provides a comprehensive survey on recent advances in deep reinforcement learning (DRL) for solving partially observable Markov decision processes (POMDP) problems—a critical challenge in real-world AI where agents must act with incomplete information. The two-part series, published in 2021, systematically reviews DRL applications across transportation, industries, communications, and networking, offering a valuable roadmap for researchers and practitioners. With 11 citations to date, this work is gaining recognition as a go-to resource for those tackling complex, partially observable environments. Zang’s contributions help bridge theoretical RL frameworks with practical deployment, highlighting how DRL can simulate human-like learning in dynamic, uncertain settings. Their survey not only synthesizes cutting-edge methods but also identifies open challenges, making it an essential reference for students and researchers aiming to push the boundaries of autonomous decision-making. Zang’s work continues to influence the development of robust, adaptive AI systems across multiple domains.
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Top Papers
- 1