AILabDsUnipi/SafeQIL-dataset

Human-Generated Demonstrations for Safe Reinforcement Learning

Paper: Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective

Code: 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.

/
├── 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.

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:

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

@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}, 
}