Summary of Maximum Manifold Capacity Representations in State Representation Learning, by Li Meng et al.
Maximum Manifold Capacity Representations in State Representation Learning
by Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad
First submitted to arxiv on: 22 May 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 In this paper, researchers leverage manifold-based self-supervised learning (SSL) to create powerful state representations in reinforcement learning. The DeepInfomax with an unbalanced atlas (DIM-UA) method has shown impressive results, but a new approach called Maximum Manifold Capacity Representation (MMCR) presents challenges due to its computational costs and lengthy pre-training times. To bridge this gap, the authors propose integrating MMCR into existing SSL methods using a discerning regularization strategy that enhances mutual information’s lower bound. They also develop a novel state representation learning method extending DIM-UA by incorporating a nuclear norm loss to enforce manifold consistency robustly. The proposed method improves upon DIM-UA in terms of mean F1 score and demonstrates gains when combined with SimCLR and Barlow Twins. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special techniques to help computers learn from large amounts of data without being explicitly taught. They’re trying to make better “maps” of how this data is connected, which can be useful for things like video games or self-driving cars. There are a few different ways they do this, and some work better than others. The main idea is that by using these special techniques, they can create more detailed and accurate maps of the data, which can help computers make better decisions. |
Keywords
» Artificial intelligence » F1 score » Regularization » Reinforcement learning » Representation learning » Self supervised
