# Int-HRL

This is the official repository for [Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning](https://perceptualui.org/publications/penzkofer23_ala/)<br> 

Int-HRL uses eye gaze from human demonstration data on the Atari game Montezuma's Revenge to extract human player's intentions and converts them to sub-goals for Hierarchical Reinforcement Learning (HRL). For further details take a look at the corresponding paper. 

## Dataset 
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset available at [https://zenodo.org/record/3451402#.Y5chr-zMK3J](https://zenodo.org/record/3451402#.Y5chr-zMK3J) <br>
![Atari-HEAD Montezuma's Revenge](supplementary/291_RZ_7364933_May-08-20-23-25.gif)

To pre-process the Atari-HEAD data run [Preprocess_AtariHEAD.ipynb](Preprocess_AtariHEAD.ipynb), yielding the `all_trials.pkl` file needed for the following steps. 

## Sub-goal Extraction Pipeline

1. [RAM State Labeling](RAMStateLabeling.ipynb): annotate Atari-HEAD data with room id and level information, as well as agent and skull location    
2. [Subgoals From Gaze](SubgoalsFromGaze.ipynb): run sub-goal proposal extraction by generating saliency maps
3. [Alignment with Trajectory](TrajectoryMatching.ipynb): run expert trajectory to get order of subgoals 

## Intention-based Hierarchical RL Agent
under construction

## Citation 
Please consider citing these paper if you use Int-HRL or parts of this repository in your research:
```
@article{penzkofer24_ncaa,
  author = {Penzkofer, Anna and Schaefer, Simon and Strohm, Florian and Bâce, Mihai and Leutenegger, Stefan and Bulling, Andreas},
  title = {Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning},
  journal = {Neural Computing and Applications (NCAA)},
  year = {2024},
  pages = {1--7},
  doi = {10.1007/s00521-024-10596-2},
  volume = {36}
}
@inproceedings{penzkofer23_ala,
  author = {Penzkofer, Anna and Schaefer, Simon and Strohm, Florian and Bâce, Mihai and Leutenegger, Stefan and Bulling, Andreas},
  title = {Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning},
  booktitle = {Proc. Adaptive and Learning Agents Workshop (ALA)},
  year = {2023},
  doi = {10.48550/arXiv.2306.11483},
  pages = {1--7}
}
```