Summary of Offline and Distributional Reinforcement Learning For Radio Resource Management, by Eslam Eldeeb and Hirley Alves
Offline and Distributional Reinforcement Learning for Radio Resource Management
by Eslam Eldeeb, Hirley Alves
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 proposed offline and distributional reinforcement learning (RL) scheme for radio resource management (RRM) in intelligent wireless networks leverages static datasets to train models without environmental interaction. This approach addresses limitations of traditional RL, which relies on online interactions, and uncertainty-averse schemes that struggle with real-world stochastic environments. The scheme considers return distributions, enabling offline training and outperforming conventional RRM models by 10% over online RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to manage wireless networks is being explored using a type of artificial intelligence called reinforcement learning (RL). Right now, RL is mostly used in situations where the AI can interact with the environment in real-time. But what if we wanted to use this AI in places where that’s not possible? That’s why researchers are proposing a new way to do RL that doesn’t require interaction with the environment. They’re testing it on managing wireless networks and finding that it outperforms traditional methods. |
Keywords
* Artificial intelligence * Reinforcement learning
