Summary of R3l: Relative Representations For Reinforcement Learning, by Antonio Pio Ricciardi et al.
R3L: Relative Representations for Reinforcement Learning
by Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 builds upon recent advancements in representation learning to develop a framework that combines components from different neural networks in Visual Reinforcement Learning. The authors adapt relative representations, which map encoder embeddings to a universal space, to this setting, enabling the creation of new agents that can effectively handle novel visual-task pairs not encountered during training. This approach has significant implications for model reuse, reducing the need for retraining and computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a way to combine parts from different neural networks in Visual Reinforcement Learning. They took an idea called relative representations, which connects encoder information to a universal space, and applied it to this area. This lets them make new agents that can handle new visual-task combinations they didn’t train on before. This will help reuse models, saving time and computer power. |
Keywords
» Artificial intelligence » Encoder » Reinforcement learning » Representation learning
