Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning

• URL: http://arxiv.org/abs/2208.14074v1
• Date: Tue, 30 Aug 2022 08:44:15 GMT
• Title: Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning
• Authors: Pihe Hu, Ling Pan, Yu Chen, Zhixuan Fang, Longbo Huang
• Abstract summary: Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing. We propose a deep reinforcement learning (DRL) algorithm, named Recurrent Softmax Delayed Deep Double Deterministic Policy Gradient ($mathttRSD4$) $mathttRSD4$ guarantees resource and delay constraints by Lagrangian dual and delay-sensitive queues, respectively. It also efficiently tackles partial observability with a memory mechanism enabled by the recurrent neural network (RNN) and introduces user-level decomposition and node-level
• Score: 28.35473469490186
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing. Yet, it poses a critical challenge since the scheduler needs to make real-time decisions to guarantee the delay and resource constraints simultaneously without prior information of system dynamics, which can be time-varying and hard to estimate. Moreover, many practical scenarios suffer from partial observability issues, e.g., due to sensing noise or hidden correlation. To tackle these challenges, we propose a deep reinforcement learning (DRL) algorithm, named Recurrent Softmax Delayed Deep Double Deterministic Policy Gradient ($\mathtt{RSD4}$), which is a data-driven method based on a Partially Observed Markov Decision Process (POMDP) formulation. $\mathtt{RSD4}$ guarantees resource and delay constraints by Lagrangian dual and delay-sensitive queues, respectively. It also efficiently tackles partial observability with a memory mechanism enabled by the recurrent neural network (RNN) and introduces user-level decomposition and node-level merging to ensure scalability. Extensive experiments on simulated/real-world datasets demonstrate that $\mathtt{RSD4}$ is robust to system dynamics and partially observable environments, and achieves superior performances over existing DRL and non-DRL-based methods.

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