Summary of The Surprising Ineffectiveness Of Pre-trained Visual Representations For Model-based Reinforcement Learning, by Moritz Schneider et al.
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
by Moritz Schneider, Robert Krug, Narunas Vaskevicius, Luigi Palmieri, Joschka Boedecker
First submitted to arxiv on: 15 Nov 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 The paper investigates the application of pre-trained visual representations (PVRs) in model-based reinforcement learning (RL), aiming to enhance sample efficiency and generalization capabilities. Specifically, it benchmarks a set of PVRs on challenging control tasks in a model-based RL setting, analyzing their impact on data efficiency, generalization, and performance. The results show that current PVRs are not more sample-efficient than learning representations from scratch, but they do generalize better to out-of-distribution (OOD) settings when considering the quality of the trained dynamics model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how using pre-trained visual representations in a special kind of machine learning called model-based reinforcement learning can help make machines learn faster and be more good at doing things on their own. They test this idea by trying out different pre-trained visual representations on some tricky control tasks and see what happens. The results show that while these pre-trained representations don’t really help with learning fast, they do help a little bit when the machine is faced with something new it’s never seen before. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Reinforcement learning
