Duplicate from george22294/SafeQIL-dataset

Co-authored-by: George Papadopoulos <george22294@users.noreply.huggingface.co>

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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+*.gif filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: mit
+viewer: false
+task_categories:
+- reinforcement-learning
+tags:
+- inverse-constrained-reinforcement-learning
+- safe-rl
+- q-learning
+- offline-rl
+- demonstrations
+size_categories:
+- 100K<n<1M
+---
+
+# Human-Generated Demonstrations for Safe Reinforcement Learning
+
+**Paper:** [Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective](https://arxiv.org/abs/2602.23816)
+
+**Code:** [AILabDsUnipi/SafeQIL](https://github.com/AILabDsUnipi/SafeQIL)
+
+
+## Dataset Description
+This dataset consists of human-generated demonstrations collected across four challenging constrained environments from the Safety-Gymnasium benchmark (`SafetyPointGoal1-v0`, `SafetyCarPush2-v0`, `SafetyPointCircle2-v0`, and `SafetyCarButton1-v0`). It is designed to train agents with **SafeQIL** (Safe Q Inverse Constrained Reinforcement Learning) to maximize the likelihood of safe trajectories in Constrained Markov Decision Processes (CMDPs) where constraints are unknown and costs are non-observable.
+
+For every step in a demonstrated trajectory, we record the full transition dynamics. Each transition is captured as a tuple containing:
+* `vector_obs`: The proprioceptive/kinematic state of the agent.
+* `vision_obs`: The pixel-based visual observation.
+* `action`: The continuous control action taken by the human demonstrator.
+* `reward`: The standard task reward received.
+* `done`: The boolean flag indicating episode termination.
+
+To ensure efficient data loading and facilitate qualitative analysis, the data is distributed across three file types:
+* **`.h5` (HDF5):** Stores the core transition tuples.
+* **`.mp4`:** Provides rendered video rollouts of the expert's behavior for visual inspection.
+* **`.txt`:** Contains summary statistics and metadata for each dataset split.
+
+
+## Dataset Structure
+The dataset is organized hierarchically by environment and dataset size.
+
+```text
+/
+├── README.md                 <- This dataset card
+├── SafetyPointGoal1-v0/
+│   ├── x1/
+│   │   ├── stats.txt         <- Dataset statistics
+│   │   ├── 0.h5              <- Human generated trajectory data
+│   │   ├── 0.mp4             <- Rendered trajectory
+│   │   ├── 1.h5
+│   │   ├── 1.mp4
+│   │   ├── 2.h5
+│   │   ├── 2.mp4
+│   │   ...
+│   │   ├── 39.h5
+│   │   └── 39.mp4
+│   ├── x2/
+│   │   ├── stats.txt
+│   │   ├── 0.h5
+│   │   ...
+│   │   └── 79.h5
+│   ├── x4/
+│   │   ├── stats.txt
+│   │   ├── 0.h5
+│   │   ...
+│   │   └── 159.h5
+│   ├── x8/
+│   │   ├── stats.txt
+│   │   ├── 0.h5
+│   │   ...
+│   │   └── 319.h5
+├── SafetyCarPush2-v0/
+│   ├── x1/
+│   │   ...
+│   │   x8/
+├── ...
+```
+
+Note that `SafetyCarButton1-v0` has only `x1` dataset. Also, note that only `x1` datasets contain video examples.
+
+## How to Use This Dataset
+
+While the dataset is a manageable ~50GB, we recommend using the `huggingface_hub` Python library to selectively download subsets of the data (e.g., a specific environment or size multiplier) to save bandwidth.
+
+```python
+from huggingface_hub import snapshot_download
+
+# Example: Download only the 'x1' dataset for SafetyPointGoal1-v0
+snapshot_download(
+    repo_id="george22294/SafeQIL-dataset", # Replace with your actual repo ID
+    repo_type="dataset",
+    allow_patterns="SafetyPointGoal1-v0/x1/*", 
+    local_dir="./demonstrations/SafetyPointGoal1-v0/x1/"
+)
+```
+
+### Loading HDF5 Files
+
+You can load the human-generated tuples directly using `h5py`. Note that the data inside each file is nested under a group named after the episode (e.g., for the file `0.h5` the group name is `episode_0`, for the file `1.h5` it is `episode_1`, etc). 
+
+You can dynamically grab this group name in Python to load the data:
+
+```python
+import h5py
+
+file_path = './local_data/SafetyPointGoal1-v0/x1/0.h5'
+
+with h5py.File(file_path, 'r') as f:
+    
+    # Load the arrays
+    vector_obs = f['episode_0']['vector_obs'][:]
+    vision_obs = f['episode_0']['vision_obs'][:]
+    actions = f['episode_0']['actions'][:]
+    reward = f['episode_0']['reward'][:]
+    done = f['episode_0']['done'][:]
+```
+
+## Citation
+
+```bibtex
+@misc{papadopoulos2026learningmaintainsafetyexpert,
+      title={Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective}, 
+      author={George Papadopoulos and George A. Vouros},
+      year={2026},
+      eprint={2602.23816},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.23816}, 
+}
+```

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