79 lines
2.1 KiB
Markdown
79 lines
2.1 KiB
Markdown
<div align="center">
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<h1> Neural Reasoning about Agents' Goals, Preferences, and Actions </h1>
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**[Matteo Bortoletto][1], [Lei Shi][2], [Andreas Bulling][3]** <br> <br>
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**AAAI'24, Vancouver, CA** <br>
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**[[Paper][4]]**
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</div>
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# Citation
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If you find our code useful or use it in your own projects, please cite our paper:
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```bibtex
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@inproceedings{bortoletto2024neural,
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title={Neural Reasoning About Agents’ Goals, Preferences, and Actions},
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author={Bortoletto, Matteo and Shi, Lei and Bulling, Andreas},
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booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
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volume={38},
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number={1},
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pages={456--464},
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year={2024}
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}
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```
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# Setup
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This code is based on the [original implementation][5] of the BIB benchmark.
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## Using `virtualenv`
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```bash
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python -m virtualenv /path/to/env
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source /path/to/env/bin/activate
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pip install -r requirements.txt
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```
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## Using `conda`
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```bash
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conda create --name <env_name> python=3.8.10 pip=20.0.2 cudatoolkit=10.2.89
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conda activate <env_name>
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pip install -r requirements_conda.txt
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pip install dgl-cu102 dglgo -f https://data.dgl.ai/wheels/repo.html
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```
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# Running the code
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## Activate the environment
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Run `source bibdgl/bin/activate`.
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## Index data
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This will create the json files with all the indexed frames for each episode in each video.
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```bash
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python utils/index_data.py
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```
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You need to manually set `mode` in the dataset class (in main).
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## Generate graphs
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This will generate the graphs from the videos:
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```bash
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python /utils/build_graphs.py --mode MODE --cpus NUM_CPUS
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```
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`MODE` can be `train`, `val` or `test`. NOTE: check `utils/build_graphs.py` to make sure you're loading the correct dataset to generate the graphs you want.
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## Training
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Use `CUDA_VISIBLE_DEVICES=0 run_train.sh`.
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## Testing
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Use `CUDA_VISIBLE_DEVICES=0 run_test.sh`.
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# Hardware setup
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All models are trained on an NVIDIA Tesla V100-SXM2-32GB GPU.
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[1]: https://mattbortoletto.github.io/
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[2]: https://perceptualui.org/people/shi/
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[3]: https://perceptualui.org/people/bulling/
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[4]: https://perceptualui.org/publications/bortoletto24_aaai.pdf
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[5]: https://github.com/kanishkg/bib-baselines
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