Hierarchical Policy Learning for Hybrid Communication Load Balancing

Kang, Jikun; Chen, Xi; Wu, Di; Xu, Yi; Li, Xue; Dudek, Gregory; Lee, Taeseop; Park, Intaik

doi:10.1109/icc42927.2021.9500379

Cited by 18 publications

(2 citation statements)

References 17 publications

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“…This avoids the need to engineer a sensible discretization scheme, and can potentially avoid undesired human bias in this process. An additional trend in recent papers is the integration of RL with other learning techniques such as clustering (i.e., Xu et al, 2019b), hierarchical learning (i.e., Kang et al, 2021), meta-learning (i.e., Feriani et al, 2022, and knowledge distillation (i.e., Li et al, 2022). These additional techniques complement the core RL method and address key challenges such as scalability, adaptability, and model generalization.…”

Section: Policy-gradient Methods For Load Balancingmentioning

confidence: 99%

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Reinforcement learning for communication load balancing: approaches and challenges

Wu,

Li,

Ferini

et al. 2023

Front. Comput. Sci.

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The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for enhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.

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Section: Policy-gradient Methods For Load Balancingmentioning

confidence: 99%

“…In Kang et al (2021), the authors propose a hierarchical policy learning framework for load balancing for both active users and idle users. Proximal policy optimization (PPO) of Schulman et al (2017) is the RL algorithm used for this work.…”

Section: Policy-gradient Methods For Load Balancingmentioning

confidence: 99%