README.md

---
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 ([email protected])
2. Edward Mehrez ([email protected])
3. Ludger Paehler ([email protected])
4. Steven L. Brunton ([email protected])

## 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}
}
```