Summary of To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning For Mobile Edge Computing, by Maddalena Boscaro et al.
To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
by Maddalena Boscaro, Federico Mason, Federico Chiariotti, Andrea Zanella
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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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 tackles the management of Mobile Edge Computing (MEC) using Artificial Intelligence (AI), specifically focusing on resource allocation for users with varying priorities and latency requirements. The authors highlight that current AI algorithms neglect the cost of learning, which can be significant in real-world scenarios. To address this, they propose a new algorithm that dynamically selects when to train a Deep Reinforcement Learning (DRL) agent to allocate resources. This method is highly general and can be applied to various scenarios involving training overheads. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI helps manage 6G networks by adapting communication and computing services to users’ needs. The paper focuses on Mobile Edge Computing, where AI allocates resources based on job priorities and latency requirements. Current AI algorithms don’t consider the cost of learning, which is significant in real-world scenarios. This paper proposes a new algorithm that considers this cost and can allocate resources effectively. |
Keywords
* Artificial intelligence * Reinforcement learning
