Summary of Xland-100b: a Large-scale Multi-task Dataset For In-context Reinforcement Learning, by Alexander Nikulin and Ilya Zisman and Alexey Zemtsov and Vladislav Kurenkov
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
by Alexander Nikulin, Ilya Zisman, Alexey Zemtsov, Vladislav Kurenkov
First submitted to arxiv on: 13 Jun 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 XLand-100B dataset is designed to address the lack of challenging benchmarks in the emerging field of in-context reinforcement learning. The dataset contains nearly 30,000 different tasks, covering 100 billion transitions and 2.5 billion episodes, which took 50,000 GPU hours to collect. It includes utilities to reproduce or expand it further. Common in-context RL baselines struggle to generalize to novel and diverse tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The XLand-100B dataset is a big step forward for in-context reinforcement learning research. It’s a massive collection of data that will help scientists test their ideas and see how well they work. The data is organized so that researchers can easily use it or add more to it. This will make it easier for people all over the world to do research in this area. |
Keywords
» Artificial intelligence » Reinforcement learning
