Online Deep Reinforcement Learning for Stochastic Queuing Network Optimization

Core Concepts

This work proposes an intervention-assisted framework that combines the learning power of neural networks with the guaranteed stability of classical control policies to enable online deep reinforcement learning for stochastic queuing network optimization.

Abstract

The content discusses the challenges of applying deep reinforcement learning (DRL) to stochastic queuing network (SQN) control tasks in an online setting, where an intelligent agent directly interacts with the real-world environment and learns an optimal control policy through these online interactions. Key highlights: Traditional DRL methods rely on offline simulations or static datasets, limiting their real-world application in SQN control. SQNs present a challenge for online DRL due to the unbounded nature of the queues within the network, resulting in an unbounded state-space. Neural networks are poor at extrapolating to unseen states in unbounded state-spaces. To address this challenge, the authors propose an intervention-assisted framework that leverages strategic interventions from known stable policies to ensure the queue sizes remain bounded. This framework combines the learning power of neural networks with the guaranteed stability of classical control policies for SQNs. The authors introduce a method to design these intervention-assisted policies to ensure strong stability of the network. They extend foundational DRL theorems for intervention-assisted policies and develop two practical algorithms specifically for online DRL of SQNs. Experiments show that the proposed algorithms outperform both classical control approaches and prior online DRL algorithms.

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Key Insights Distilled From

Intervention-Assisted Policy Gradient Methods for Online Stochastic Queuing Network Optimization

by Jerrod Wigmo... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04106.pdf

Deeper Inquiries

How can the intervention-assisted framework be extended to handle more complex network topologies or additional constraints beyond queue stability

The intervention-assisted framework can be extended to handle more complex network topologies or additional constraints by adapting the partitioning of the state-space and the intervention policy. For complex network topologies, the learning region S𝜃 can be defined to capture the specific characteristics of the network, such as the number of nodes, links, or traffic classes. By carefully designing the learning region to encapsulate the essential features of the network, the intervention-assisted policy can effectively learn and optimize control strategies for these complex topologies. Additionally, additional constraints beyond queue stability can be incorporated by modifying the intervention policy 𝜋0 to account for these constraints. For example, if there are constraints on link capacities or traffic priorities, the intervention policy can be designed to ensure these constraints are met during interventions. By customizing the intervention policy and the partitioning of the state-space, the intervention-assisted framework can be tailored to handle a wide range of complex network topologies and constraints.

What are the potential drawbacks or limitations of relying on a known stable policy for the intervention component of the framework

One potential drawback of relying on a known stable policy for the intervention component of the framework is the risk of suboptimal performance. While a known stable policy can ensure stability and guide the learning process, it may not necessarily lead to the optimal control strategy for the specific network environment. The intervention policy may be conservative or limited in its exploration of the state-space, potentially hindering the discovery of more efficient or effective control policies. Additionally, the reliance on a known stable policy may restrict the adaptability of the intervention-assisted framework to dynamic or changing network conditions. If the known stable policy is not well-suited to the current network dynamics, it may impede the learning process and limit the overall performance of the intervention-assisted approach. Therefore, careful consideration and tuning of the intervention policy are essential to mitigate these limitations and ensure optimal performance.

How might the intervention-assisted approach be adapted to address other types of control problems beyond stochastic queuing networks

The intervention-assisted approach can be adapted to address other types of control problems beyond stochastic queuing networks by customizing the intervention policy and the partitioning of the state-space to suit the specific characteristics of the control problem. For example, in robotics or autonomous systems, the intervention policy can be designed to incorporate safety constraints or task-specific requirements. By defining the learning region and intervention region based on the unique features of the control problem, the intervention-assisted framework can effectively learn and optimize control policies in diverse domains. Additionally, the policy gradient methods and trust-region algorithms used in the intervention-assisted approach can be applied to a wide range of control problems, providing a flexible and scalable framework for online learning and optimization. By adapting the intervention-assisted approach to different control problems, it can offer a versatile and powerful method for training intelligent agents in various domains.