---
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}, 
}
```