Summary of Sindy-rl: Interpretable and Efficient Model-based Reinforcement Learning, by Nicholas Zolman et al.
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
by Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Brunton
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Dynamical Systems (math.DS); Optimization and Control (math.OC)
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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 presents a framework called SINDy-RL that combines sparse dictionary learning (SINDy) with deep reinforcement learning (DRL). The goal is to create efficient, interpretable, and trustworthy models for controlling complex systems. Traditional DRL methods require many training examples and can result in black-box policies that are difficult to understand or implement on embedded systems. SINDy-RL addresses these limitations by using SINDy to identify the underlying dynamics of a system, then applying DRL to optimize control policies. The approach is demonstrated on benchmark control environments and challenging fluids problems, achieving comparable performance to state-of-the-art DRL algorithms while using fewer interactions and resulting in smaller, more interpretable policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes artificial intelligence (AI) better for controlling things that are hard to understand, like a special kind of reactor or the way water moves. Right now, AI is good at making decisions but not very good at explaining why it made those decisions. The new approach combines two different ways of doing AI: one that helps figure out how something works and another that helps make good choices. This new combination makes AI better for controlling things in a way that’s easy to understand. |
Keywords
* Artificial intelligence * Reinforcement learning
