Update README.md

README.md

@@ -1,3 +1,47 @@
----
-license: mit
----
+---
+license: mit
+tags:
+- Imitation Learning
+- Expert Trajectory
+- InvertedPendulum-v2
+pretty_name: InvertedPendulum-v2 Expert Dataset
+size_categories:
+- 1M<n<10M
+---
+
+# InvertedPendulum-v2 - Continuous Imitation Learning from Observation
+
+This dataset was created for the paper Explorative imitation learning: A path signature approach for continuous environments.
+It is based on InvertedPendulum-v2, which is an older version for the MuJoCo environment.
+If you would like to use newer version, be sure to check: [IL-Datasets](https://github.com/NathanGavenski/IL-Datasets) repository for the updated list.
+
+## Description
+
+The dataset consists of 10 episodes with an average episodic reward of `1000.0`.
+Each entry consists of:
+```
+obs (list): observation with length 2.
+action (int): action (0 or 1).
+reward (float): reward point for that timestep.
+episode_starts (bool): if that state was the initial timestep for an episode.
+```
+
+## Usage
+
+Feel free to download and use the `teacher.jsonl` dataset as you please.
+If you are interested in using our PyTorch Dataset implementation, feel free to check the [IL Datasets](https://github.com/NathanGavenski/IL-Datasets/blob/main/src/imitation_datasets/dataset/dataset.py) project.
+There, we implement a base Dataset that downloads this dataset and all other datasets directly from HuggingFace.
+The Baseline Dataset also allows for more control over train and test splits and how many episodes you want to use (in cases where the 1k episodes are not necessary).
+
+## Citation
+
+```{bibtex}
+@incollection{gavenski2024explorative,
+	title={Explorative Imitation Learning: A Path Signature Approach for Continuous Environments},
+	author={Gavenski, Nathan and Monteiro, Juarez and Meneguzzi, Felipe and Luck, Michael and Rodrigues, Odinaldo},
+	booktitle={ECAI 2024},
+	pages={}
+	year={2024},
+	publisher={IOS Press}
+}
+```