Summary of Continual Model-based Reinforcement Learning For Data Efficient Wireless Network Optimisation, by Cengis Hasan et al.
Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation
by Cengis Hasan, Alexandros Agapitos, David Lynch, Alberto Castagna, Giorgio Cruciata, Hao Wang, Aleksandar Milenovic
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The authors propose a novel approach for optimizing wireless network sites’ performance through continual reinforcement learning. They tackle the issue of long lead times required to deploy cell-level parameter optimization policies at new sites by formulating throughput optimization as a sequence of action spaces, represented by overlapping subsets of cell-level configuration parameters provided by domain experts. The proposed system shortens the end-to-end deployment lead time by two-fold compared to a baseline approach without sacrificing optimization gain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to make wireless networks better by using artificial intelligence. Normally, it takes a long time to set up new network sites because we need to retrain the AI every time. The authors found a way to use “continual reinforcement learning” to optimize the network in small steps, so we don’t have to start from scratch every time. This makes the process faster and more efficient. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
