Summary of Actor-critic Physics-informed Neural Lyapunov Control, by Jiarui Wang and Mahyar Fazlyab
Actor-Critic Physics-informed Neural Lyapunov Control
by Jiarui Wang, Mahyar Fazlyab
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); 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 The proposed method trains a stabilizing neural network controller along with its corresponding Lyapunov certificate to maximize the resulting region of attraction while respecting actuation constraints, using Zubov’s Partial Differential Equation (PDE) to characterize the true region of attraction. The framework follows an actor-critic pattern, alternating between improving the control policy and learning a Zubov function. This approach is applied to several design problems, achieving consistent and significant improvements in the size of the resulting region of attraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make sure controllers work well even when there are unexpected changes or uncertainties. It does this by training a special kind of neural network that takes into account these uncertainties. The goal is to find a controller that can keep things stable and safe, while also making sure it doesn’t get too close to the edge and fall apart. The researchers use a special equation called Zubov’s PDE to figure out how big this “stable zone” should be. They then test their method on different problems and show that it works really well. |
Keywords
* Artificial intelligence * Neural network
