# A Novel Switch-Type Policy Network for Resource Allocation Problems: Technical Report

arXiv: https://arxiv.org/abs/2501.11136 · HTML: https://mlsanity.com/paper/2501.11136/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 19 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [A Survey of Zero-shot Generalisation in Deep Reinforcement Learning](https://mlsanity.com/paper/2111.09794/) (2021)
- [A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning](https://mlsanity.com/paper/1806.07937/) (2018)
- [Certified Monotonic Neural Networks](https://mlsanity.com/paper/2011.10219/) (2020)
- [The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/2109.02869/) (2021)
- [Neuroevolution of Self-Interpretable Agents](https://mlsanity.com/paper/2003.08165/) (2020)
- [Queueing Network Controls via Deep Reinforcement Learning](https://mlsanity.com/paper/2008.01644/) (2020)
- [Neuro-algorithmic Policies enable Fast Combinatorial Generalization](https://mlsanity.com/paper/2102.07456/) (2021)
- [Dynamic priority allocation via restless bandit marginal productivity indices](https://mlsanity.com/paper/2304.06115/) (2023)
- [Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service](https://mlsanity.com/paper/2101.04627/) (2021)
- [Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals](https://mlsanity.com/paper/1905.07020/) (2019)
- [Scheduling Algorithms for Minimizing Age of Information in Wireless Broadcast Networks with Random Arrivals: The No-Buffer Case](https://mlsanity.com/paper/1712.07419/) (2017)
- [A unifying computations of Whittle's Index for Markovian bandits](https://mlsanity.com/paper/1906.10946/) (2019)
- [Intervention-Assisted Policy Gradient Methods for Online Stochastic Queuing Network Optimization: Technical Report](https://mlsanity.com/paper/2404.04106/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning](https://mlsanity.com/paper/2604.03605/) (2026)

## Abstract (arXiv)

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 contributions: first, the development of the STN as a more effective alternative to MLPs; second, empirical evidence showing that STNs achieve superior sample efficiency in various training scenarios; and third, experimental results demonstrating that STNs match MLP performance in familiar environments and significantly outperform them in new settings. By embedding domain-specific knowledge, the STN enhances the Proximal Policy Optimization (PPO) algorithm's effectiveness without compromising performance, suggesting its suitability for a wide range of queueing network control problems.