A Novel Switch-Type Policy Network for Resource Allocation Problems: Technical Report
Jerrod Wigmore, Brooke Shrader, Eytan Modiano

TL;DR
This paper introduces a switch-type neural network architecture that enhances the efficiency and generalization of deep reinforcement learning policies for queueing network control, outperforming traditional MLPs especially in unseen environments.
Contribution
The paper presents the novel switch-type neural network (STN) architecture, demonstrating its superior sample efficiency and generalization over MLPs in queueing network control tasks.
Findings
STNs achieve better sample efficiency than MLPs.
STNs match MLP performance in familiar environments.
STNs outperform MLPs in new, unseen environments.
Abstract
Deep Reinforcement Learning (DRL) has become a powerful tool for developing control policies in queueing networks, but the common use of Multi-layer Perceptron (MLP) neural networks in these applications has significant drawbacks. MLP architectures, while versatile, often suffer from poor sample efficiency and a tendency to overfit training environments, leading to suboptimal performance on new, unseen networks. In response to these issues, we introduce a switch-type neural network (STN) architecture designed to improve the efficiency and generalization of DRL policies in queueing networks. The STN leverages structural patterns from traditional non-learning policies, ensuring consistent action choices across similar states. This design not only streamlines the learning process but also fosters better generalization by reducing the tendency to overfit. Our works presents three key…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Age of Information Optimization
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