Summary of Intervention-assisted Policy Gradient Methods For Online Stochastic Queuing Network Optimization: Technical Report, by Jerrod Wigmore et al.
Intervention-Assisted Policy Gradient Methods for Online Stochastic Queuing Network Optimization: Technical Report
by Jerrod Wigmore, Brooke Shrader, Eytan Modiano
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to training neural network control policies for stochastic queuing networks (SQN) using Online Deep Reinforcement Learning-based Controls (ODRLC). Unlike traditional DRL methods, ODRLC learns an optimal control policy by interacting directly with the real environment. However, SQNs pose a challenge due to their unbounded state-space, which can be difficult for neural networks to extrapolate to unseen states. To address this issue, the authors propose an intervention-assisted framework that leverages strategic interventions from known stable policies to ensure queue sizes remain bounded. The proposed method combines the learning power of neural networks with the guaranteed stability of classical control policies. Two practical algorithms are developed specifically for ODRLC of SQNs, and experiments demonstrate that these algorithms outperform both classical control approaches and prior ODRLC algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about finding a new way to control complex systems called stochastic queuing networks (SQN). Traditional methods can’t be used because they rely on simulations or data from the past. The new approach, called Online Deep Reinforcement Learning-based Controls (ODRLC), lets an intelligent agent learn by interacting directly with the real environment. This is tricky because SQNs have an endless number of states, making it hard for computers to learn from them. To solve this problem, the researchers created a special framework that uses known stable policies to keep the queues in check. They also developed two new algorithms specifically for controlling SQNs and tested them, finding they work better than other methods. |
Keywords
* Artificial intelligence * Neural network * Reinforcement learning
