Summary of Locality Sensitive Sparse Encoding For Learning World Models Online, by Zichen Liu et al.
Locality Sensitive Sparse Encoding for Learning World Models Online
by Zichen Liu, Chao Du, Wee Sun Lee, Min Lin
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach revisits linear regression models supported by nonlinear random features for model-based reinforcement learning (MBRL) in non-stationary environments. The goal is to achieve Follow-The-Leader (FTL) online learning, which optimally fits all previous experiences at each round. To efficiently update the world model while trading off capacity and computation efficiency, a locality sensitive sparse encoding is introduced, allowing for sparse updates even with high-dimensional nonlinear features. The approach is validated through experiments on Dyna MBRL settings, demonstrating performance comparable to or surpassing deep world models trained with replay and other continual learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn more about the internet! Researchers are working on making artificial intelligence (AI) better at adapting to changing situations, like when we’re learning new things online. They want AI to be able to remember what it learned before, even if the situation changes. This is important because it can help AI make better decisions and solve problems. The researchers came up with a new way of doing this using linear regression models that can learn quickly and efficiently. They tested their approach and found that it works just as well as other methods, but it’s faster and uses less computer power. |
Keywords
* Artificial intelligence * Continual learning * Linear regression * Online learning * Reinforcement learning
