Push up entire data without the Koopman tensors for now.

README.md

@@ -1,3 +1,103 @@
 ---
+annotations_creators: []
+language:
+- code
 license: cc-by-4.0
+pretty_name: KoopmanRL
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- reinforcement-learning
+task_ids: []
 ---
+
+# Dataset Card for KoopmanRL: Koopman-infused Reinforcement Learning
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+- [Dataset Summary](#dataset-summary)
+- [Dataset Structure](#dataset-structure)
+- [Reproducing Plots](#reproducing-plots)
+- [Usage of the Dataset](#usage-of-the-dataset)
+- [Licensing](#licensing)
+- [Contact Info](#contact-info)
+- [How to Cite](#how-to-cite)
+
+## Dataset Description
+
+- **Homepage:** https://dynamicslab.github.io/KoopmanRL-NeurIPS/
+- **Paper:** https://arxiv.org
+- **Leaderboard:** N/A 
+
+## Dataset Summary
+
+This dataset contains the collected experimental data used for the results of _Koopman-Assisted Reinforcement Learning_ allowing for the full reproduction, and further use of the paper's results. To reproduce the results by running the experiments yourself, please see the [source code](https://github.com/Pdbz199/Koopman-RL) of KoopmanRL.
+
+## Dataset Structure
+
+The dataset of the reinforcement learning experiments for KoopmanRL contains roughly 461MB of Tensorboard files, and saved policies.
+
+| Experiment | Size | Purpose |
+|------------|------|---------|
+| Episodic Returns | 161MB | Episodic returns of all 5 considered algorithms across all 4 environments |
+| Interpretability | 55MB | Inspection of the interpretability introduced by KoopmanRL |
+| AblationSKVIBatchSize | 3.4MB | Ablation of the sensitivity to the chosen batch size |
+| AblationSKVICompute | 21MB | Ablation of the sensitivity to the amount of compute used for the construction of the Koopman tensor |
+| AblationSAKCMonoid | 86MB | Ablation of the sensitivity to the order of the monoids used for the construction of the dictionaries of the Koopman tensor |
+| AblationSAKCCompute | 134MB | Ablation of the sensitivity to the amount of compute used for the construction of the Koopman tensor |
+
+In addition the already extracted dataframes are provided. All experiments are stored as Tensorboard files, with the extracted episodic returns stores in `.parquet.gz` data frames for use with [Pandas](https://pandas.pydata.org/docs/index.html), and saved policies stored in `.pt` files.
+
+## Reproducing Plots
+
+All plots can be reproduced with the respective Jupyter notebooks, which can be found in the order of appearance in the paper:
+
+* [Episodic Returns](https://github.com/ludgerpaehler/KoopmanRLBenchmarking/blob/master/evaluations/episodic_returns.ipynb)
+* [Zoomed-in Episodic Returns of the Fluid Flow and Double Well](https://github.com/ludgerpaehler/KoopmanRLBenchmarking/blob/master/evaluations/zoomed_in.ipynb)
+* [Zoomed-in Episodic Returns of the Linear System](https://github.com/ludgerpaehler/KoopmanRLBenchmarking/blob/master/evaluations/zoomedin_linear.ipynb)
+* [Interpretability Plots & Numbers](https://github.com/ludgerpaehler/KoopmanRLBenchmarking/blob/master/evaluations/interpretability.ipynb)
+* [Ablation Heatmaps](https://github.com/ludgerpaehler/KoopmanRLBenchmarking/blob/master/evaluations/ablation_heatmaps.ipynb)
+
+## Usage of the Dataset
+
+The dataset can easiest be used with the [HuggingFace Datasets Library](https://huggingface.co/docs/datasets/index), with which one is able to either download the entire dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("dynamicslab/KoopmanRL")
+```
+
+or a desired subparts of the dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("dynamicslab/KoopmanRL", data_dir="data/EpisodicReturns")
+```
+
+## Licensing
+
+The entire dataset is licensed under a [CC-BY-4.0 license](https://spdx.org/licenses/CC-BY-4.0.html).
+
+## Contact Info
+
+1. Preston Rozwood (pwr36@cornell.com)
+2. Edward Mehrez (ejm322@cornell.edu)
+3. Ludger Paehler (paehlerludger@gmail.com)
+4. Steven L. Brunton (sbrunton@uw.edu)
+
+## How to Cite
+
+Please cite the dataset in the following format:
+
+```bibtex
+@article{rozwood2024koopman,
+  title={Koopman-Assisted Reinforcement Learning},
+  author={Rozwood, Preston and Mehrez, Edward and Paehler, Ludger and Sun, Wen and Brunton, Steven L.},
+  journal={arXiv preprint arXiv:tbd},
+  year={2024}
+}
+```

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