Summary of Regret Analysis Of Multi-task Representation Learning For Linear-quadratic Adaptive Control, by Bruce D. Lee et al.
Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
by Bruce D. Lee, Leonardo F. Toso, Thomas T. Zhang, James Anderson, Nikolai Matni
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 investigates the application of representation learning in dynamic settings for linear-quadratic control. The authors analyze the regret of multi-task representation learning and demonstrate that it can lead to a benefit in certain scenarios. In particular, they show that as the number of agents increases, the regret decreases. This is particularly important for robotics or controls applications where changing environments and goals are common. The authors also discuss the challenges of accounting for misspecification and devising novel schemes for parameter updates. Notably, sharing a representation across tasks can lead to a reduction in task-specific parameters, making it more efficient. The results are validated through numerical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make machines learn from many different experiences or environments. Typically, this is done by learning features that apply to all situations. However, what if the situation changes while the machine is still learning? This paper shows that even in these dynamic settings, using learned features can be beneficial. The authors tested their approach on a special type of control problem and found that it works well when there are many agents (like robots) involved. They also discussed some challenges they faced, like how to handle situations where the machine doesn’t quite fit what it’s learning. Overall, this research could lead to more efficient and effective machines in complex real-world scenarios. |
Keywords
* Artificial intelligence * Multi task * Representation learning
