diff --git a/README.md b/README.md index b187bb7e7d837a367ccd0862441947ad412c77f7..a058444ea962467827c5b9a07a27252b3eca7dec 100644 --- a/README.md +++ b/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} +} +``` diff --git a/data/AblationSAKCCompute/DoubleWell/NumPaths_100_NumStepsPerPath_100/DoubleWell-v0__value_based_sac_continuous_action__koopman__num_paths_100_num_steps_per_path_100__103__1708017871/events.out.tfevents.1708017871.corona261.496416.0 b/data/AblationSAKCCompute/DoubleWell/NumPaths_100_NumStepsPerPath_100/DoubleWell-v0__value_based_sac_continuous_action__koopman__num_paths_100_num_steps_per_path_100__103__1708017871/events.out.tfevents.1708017871.corona261.496416.0 new file mode 100644 index 0000000000000000000000000000000000000000..19be9592159e835f6cf583d23818c866aba8cdef --- /dev/null +++ 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