| --- |
| license: mit |
| datasets: |
| - reeeemo/jigsaw_puzzle |
| pipeline_tag: reinforcement-learning |
| library_name: stable-baselines3 |
| --- |
| |
| Reinforcement-Learning model *(puzzler-v0)* that utilizes MaskablePPO to guide assembly of a jigsaw puzzle (puzzle pieces with irregular, convex boundaries). |
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| Code to train/create this custom environment can be found in the "How Puzzling!" [github repo](https://github.com/reeeeemo/how-puzzling). |
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| **Initialization Parameters:** |
| ``` |
| def __init__(self, images, seg_model_path, max_steps=100, device="cpu") |
| ``` |
| - *images*: **list[np.ndarray]** |
| - List of images that **MUST BE SAME SAME, SAME ORIENTATION (3x3, 4x4 puzzles, etc.)**. |
| - Each will be randomly initialized every `.reset()` of the environment |
| - *seg_model_path*: **string** |
| - Path to [image segmentation weight folder](https://huggingface.co/reeeemo/puzzle-segment-model) |
| - Note that any image segmentation that segments puzzle pieces will work, however they must be axis aligned |
| - The segmentation model is used to initialize another custom model found in the same github repo, [here](https://github.com/reeeeemo/how-puzzling/blob/main/model/model.py) |
| - *max_steps*: **int** |
| - Max number of steps allowed |
| - Using MaskablePPO solves the issue of infinite actions, but if you decide to use PPO, ensure `max_steps` is set to the max number of puzzle pieces |
| - *device*: **string** |
| - CPU or GPU usage |
| |
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| Training data can be found in [events.out.tfevents](./events.out.tfevents.0.500k_5_images_norm_rew) using `tensorboard --logdir .` after downloading the repo. |
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| The environment was trained with `VecNormalize` from the `stable_baselines3` libary, you can load from [vec_normalize.pkl](./vec_normalize.pkl) |
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