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Multi-User Delay-Constrained Scheduling With Deep Recurrent Reinforcement Learning
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DOI：10.1109/TNET.2024.3359911.png)
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Multi-user delay-constrained scheduling is a crucial challenge in various real-world applications, such as wireless communication, live streaming, and cloud computing. The scheduler must make real-time decisions to guarantee both delay and resource constraints simultaneously, without prior information on system dynamics that can be time-varying and challenging to estimate. Additionally, many practical scenarios suffer from partial observability issues due to sensing noise or hidden correlation. To address these challenges, we propose a deep reinforcement learning (DRL) algorithm called Recurrent Softmax Delayed Deep Double Deterministic Policy Gradient (RSD4) (https://github.com/hupihe/RSD4), which is a data-driven method based on a Partially Observed Markov Decision Process (POMDP) formulation. RSD4 guarantees resource and delay constraints by Lagrangian dual and delay-sensitive queues, respectively. It also efficiently handles partial observability with a memory mechanism enabled by the recurrent neural network (RNN). Moreover, it introduces user-level decomposition and node-level merging to support large-scale multihop scenarios. Extensive experiments on simulated and real-world datasets demonstrate that RSD4 is robust to system dynamics and partially observable environments and achieves superior performance over existing methods.
Keyword:
Dynamic scheduling
Optimal scheduling
System dynamics
Reinforcement learning
Optimization
Delays
Observability
Delay-constrained
scheduling
partial observability
deep reinforcement learning
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3.6
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4.4K
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