Summary of Transfer Learning For a Class Of Cascade Dynamical Systems, by Shima Rabiei et al.
Transfer Learning for a Class of Cascade Dynamical Systems
by Shima Rabiei, Sandipan Mishra, Santiago Paternain
First submitted to arxiv on: 9 Oct 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 Medium Difficulty Summary: This paper investigates the problem of transfer learning in reinforcement learning, focusing on training policies for reduced-order systems and deploying them in full-state systems to alleviate computational complexity issues. The researchers consider a class of cascade dynamical systems where certain states’ dynamics influence others but not vice-versa. They develop a reinforcement learning policy that learns to ignore these influenced states and handles them using a classic controller (e.g., PID) in the full-state system. The authors provide transfer guarantees that depend on the stability of the inner loop controller, supported by numerical experiments on a quadrotor. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper is about how computers can learn to make good decisions without needing to run simulations for too long. They want to train computers to work in complex systems where some parts interact with others but not directly. The computer learns to ignore those interactions and handles them separately. The authors show that this approach works and provide guarantees that the computer will make good decisions. |
Keywords
» Artificial intelligence » Reinforcement learning » Transfer learning
