Summary of Task Adaptation Of Reinforcement Learning-based Nas Agents Through Transfer Learning, by Amber Cassimon et al.
Task Adaptation of Reinforcement Learning-based NAS Agents through Transfer Learning
by Amber Cassimon, Siegfried Mercelis, Kevin Mets
First submitted to arxiv on: 2 Dec 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 proposed novel paradigm for reinforcement learning-based neural architecture search (NAS) agents enables incremental improvement of a given architecture, allowing for more efficient training. This paper evaluates the transferability of these agents between different tasks using the Trans-NASBench-101 benchmark, assessing their efficacy and training time. The results show that pretraining an agent on one task can improve its performance in another task, with significant shortening of the training procedure possible. Transfer learning is demonstrated to be effective in reducing computational costs for NAS-based reinforcement learning agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning-based neural architecture search (NAS) agents are getting better at improving themselves. Researchers tested how well these agents can learn from one task and apply that knowledge to another task. They used a special test called Trans-NASBench-101 to see how well the agents did. The results show that when an agent is trained on one task, it gets better at doing another task too! This can help reduce the time it takes to train these agents. |
Keywords
» Artificial intelligence » Pretraining » Reinforcement learning » Transfer learning » Transferability
