52 lines
2.9 KiB
Markdown
52 lines
2.9 KiB
Markdown
# Int-HRL
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This is the official repository for [Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning](https://collaborative-ai.org/publications/penzkofer24_ncaa/)<br>
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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.
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## Dataset
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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>
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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.
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## Sub-goal Extraction Pipeline
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1. [RAM State Labeling](RAMStateLabeling.ipynb): annotate Atari-HEAD data with room id and level information, as well as agent and skull location
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2. [Subgoals From Gaze](SubgoalsFromGaze.ipynb): run sub-goal proposal extraction by generating saliency maps
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3. [Alignment with Trajectory](TrajectoryMatching.ipynb): run expert trajectory to get order of subgoals
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## Intention-based Hierarchical RL Agent
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The Int-HRL agent is based on the hierarchically guided Imitation Learning method (hg-DAgger/Q), where we adapted code from [https://github.com/hoangminhle/hierarchical_IL_RL](https://github.com/hoangminhle/hierarchical_IL_RL) <br>
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Due to the novel sub-goal extraction pipeline, our agent does not require experts during training and is more than three times more sample efficient compared to hg-DAgger/Q. <br>
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To run the full agent with 12 separate low-level agents for sub-goal execution, run [agent/run_experiment.py](agent/run_experiment.py), for single agents (one low-level agent for all sub-goals) run [agent/single_agent_experiment.py](agent/single_agent_experiment.py).
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## Extension to Venture and Hero
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under construction
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## Citation
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Please consider citing these paper if you use Int-HRL or parts of this repository in your research:
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```
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@article{penzkofer24_ncaa,
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author = {Penzkofer, Anna and Schaefer, Simon and Strohm, Florian and Bâce, Mihai and Leutenegger, Stefan and Bulling, Andreas},
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title = {Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning},
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journal = {Neural Computing and Applications (NCAA)},
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year = {2024},
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pages = {1--7},
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doi = {10.1007/s00521-024-10596-2},
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volume = {36}
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}
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@inproceedings{penzkofer23_ala,
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author = {Penzkofer, Anna and Schaefer, Simon and Strohm, Florian and Bâce, Mihai and Leutenegger, Stefan and Bulling, Andreas},
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title = {Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning},
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booktitle = {Proc. Adaptive and Learning Agents Workshop (ALA)},
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year = {2023},
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doi = {10.48550/arXiv.2306.11483},
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pages = {1--7}
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}
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```
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