Adaptive multi-layer reward based Q-learning for source tracing in diffusive molecular communications environment with obstacles

Lou, Zhibo; Zhang, Wence; Bao, Xu

doi:10.1016/j.nancom.2023.100478

Nano Communication Networks

2023

DOI: 10.1016/j.nancom.2023.100478

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Adaptive multi-layer reward based Q-learning for source tracing in diffusive molecular communications environment with obstacles

Zhibo Lou,

Wence Zhang,

Xu Bao

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2024

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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Yang,

Gao,

Ding

et al. 2024

WIREs Data Min & Knowl

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This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space.This article is categorized under: Technologies > Computational Intelligence Technologies > Artificial Intelligence

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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Yang,

Gao,

Ding

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Adaptive multi-layer reward based Q-learning for source tracing in diffusive molecular communications environment with obstacles

Cited by 1 publication

References 16 publications

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

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