Summary of Sit: Symmetry-invariant Transformers For Generalisation in Reinforcement Learning, by Matthias Weissenbacher et al.
SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning
by Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
First submitted to arxiv on: 21 Jun 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 A novel approach to reinforcement learning (RL) is proposed, which enables the effective deployment of trained policies to new or similar environments. The Symmetry-Invariant Transformer (SiT) leverages local and global data patterns in a self-supervised manner to improve generalization. This is achieved through Graph Symmetric Attention, which refines self-attention mechanisms to preserve graph symmetries, resulting in invariant latent representations. SiT outperforms vision transformers on MiniGrid and Procgen RL benchmarks and demonstrates sample efficiency on Atari 100k and CIFAR10. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re playing a game where the rules change slightly every time you play. You’d want to learn how to adapt quickly, right? This is what reinforcement learning (RL) is all about – teaching machines to make good decisions in new situations. The problem is that most RL systems struggle when faced with environments that are similar but not identical. That’s where the Symmetry-Invariant Transformer (SiT) comes in. It’s a clever way to analyze patterns in data and learn from them, even if the environment changes slightly. SiT is really good at generalizing – it can apply what it learned to new situations with ease. This is important because it means machines could be more flexible and efficient in real-world applications. |
Keywords
» Artificial intelligence » Attention » Generalization » Reinforcement learning » Self attention » Self supervised » Transformer
