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--- |
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language: en |
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tags: |
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- machine-learning |
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- reinforcement-learning |
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- sokoban |
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- planning |
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license: apache-2.0 |
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--- |
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# Trained learned planners |
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This repository contains the trained networks from the paper ["Planning behavior in a recurrent neural network that |
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plays Sokoban"](https://openreview.net/forum?id=T9sB3S2hok), presented at the ICML 2024 Mechanistic Interpretability |
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Workshop. |
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To load and use the NNs, please refer to the [learned-planner |
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repository](http://github.com/alignmentresearch/learned-planner), and possibly to the [training code |
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](https://github.com/AlignmentResearch/train-learned-planner). |
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# Model details |
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## Hyperparameters: |
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See `model/*/cp_*/cfg.json` for the hyperparameters that were used to train a particular run. |
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## Best Models: |
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The best models for each of the model type are stored in the following directory: |
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| Model | Directory | Parameter Count | |
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|:-------|:-----------|:-----------------| |
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| DRC(3, 3) | `drc33/bkynosqi/cp_2002944000` | 1,285,125 (1.29M) | |
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| DRC(1, 1) | `drc11/eue6pax7/cp_2002944000` | 987,525 (0.99M) | |
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| ResNet | `resnet/syb50iz7/cp_2002944000` | 3,068,421 (3.07M) | |
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## Probes & SAEs: |
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The trained probes and SAEs are stored in the `probes` and `saes` directories, respectively. |
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## Training dataset: |
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The [Boxoban set of levels by DeepMind](https://github.com/google-deepmind/boxoban-levels). |
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# Citation |
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If you use any of these artifacts, please cite our work: |
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```bibtex |
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@inproceedings{garriga-alonso2024planning, |
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title={Planning behavior in a recurrent neural network that plays Sokoban}, |
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author={Adri{\`a} Garriga-Alonso and Mohammad Taufeeque and Adam Gleave}, |
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booktitle={ICML 2024 Workshop on Mechanistic Interpretability}, |
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year={2024}, |
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url={https://openreview.net/forum?id=T9sB3S2hok} |
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} |
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``` |
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