| --- |
| viewer: false |
| tags: |
| - reinforcement-learning |
| - in-context |
| - imitation-learning |
| - generalist-agent |
| license: apache-2.0 |
| task_categories: |
| - reinforcement-learning |
| --- |
| # Vintix II Cross-Domain ICRL Dataset |
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| ## Dataset Summary |
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| This dataset is a large-scale cross-domain benchmark for **in-context reinforcement learning** and **continuous control**. It was introduced with **Vintix II** and covers a diverse set of tasks spanning robotic manipulation, dexterous control, locomotion, energy management, industrial process control, autonomous driving, and other control settings. |
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| The training set contains **209 tasks across 10 domains**, totaling **3.8M episodes** and **709.7M timesteps**. In addition, the benchmark defines **46 held-out tasks** for evaluation on unseen tasks and environment variations. |
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| | Domain | Tasks | Episodes | Timesteps | Sample Weight | |
| |---|---:|---:|---:|---:| |
| | Industrial-Benchmark | 16 | 288k | 72M | 10.1% | |
| | Bi-DexHands | 15 | 216.2k | 31.7M | 4.5% | |
| | Meta-World | 45 | 670k | 67M | 9.4% | |
| | Kinetix | 42 | 1.1M | 62.8M | 8.9% | |
| | CityLearn | 20 | 146.4k | 106.7M | 15.0% | |
| | ControlGym | 9 | 230k | 100M | 14.1% | |
| | HumEnv | 12 | 120k | 36M | 5.1% | |
| | MuJoCo | 11 | 665.1k | 100M | 14.1% | |
| | SinerGym | 22 | 42.3k | 30.9M | 4.4% | |
| | Meta-Drive | 17 | 271.9k | 102.6M | 14.4% | |
| | **Overall** | **209** | **3.8M** | **709.7M** | **100%** | |
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| ## Dataset Structure |
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| The dataset is stored as a collection of **`.h5` files**, where each file corresponds to a single trajectory from a specific environment. |
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| Each trajectory file is split into groups of **10,000 steps**, except for the final group, which may contain fewer steps. |
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| Every group contains the following fields: |
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| - **`proprio_observation`**: sequence of observations (`np.float32`) |
| - **`action`**: sequence of actions executed in the environment (`np.float32`) |
| - **`reward`**: sequence of rewards received after each action (`np.float32`) |
| - **`step_num`**: step indices within the episode (`np.int32`) |
| - **`demonstrator_action`**: sequence of demonstrator actions corresponding to the observations |
| |
| This layout is designed for efficient storage and loading of long trajectories while preserving both collected behavior and demonstrator supervision. |
