Update README.md

README.md

@@ -1,6 +1,33 @@
+---
+annotations_creators:
+- machine-generated
+language: []
+language_creators:
+- expert-generated
+license:
+- mit
+multilinguality: []
+pretty_name: How Resilient are Imitation Learning Methods to Sub-Optimal Experts?
+size_categories:
+- 100B<n<1T
+source_datasets:
+- original
+tags:
+- Imitation Learning
+- Expert Trajectories
+- Classic Control
+task_categories:
+- other
+task_ids: []
+---
+
 # How Resilient are Imitation Learning Methods to Sub-Optimal Experts?
+
+## Related Work
 Trajectories used in [How Resilient are Imitation Learning Methods to Sub-Optimal Experts?]()
+The code that uses this data is on GitHub: https://github.com/NathanGavenski/How-resilient-IL-methods-are
 
+# Structure
 These trajectories are formed by using [Stable Baselines](https://stable-baselines.readthedocs.io/en/master/).
 Each file is a dictionary of a set of trajectories with the following keys:
 
@@ -8,10 +35,22 @@ Each file is a dictionary of a set of trajectories with the following keys:
 * obs: current state in the given timestamp `t`
 * rewards: reward retrieved after the action in the given timestamp `t`
 * episode_returns: The aggregated reward of each episode (each file consists of 5000 runs)
-* episode_Starts: Whether that `obs` is the first state of an episode (boolean list).
+* episode_Starts: Whether that `obs` is the first state of an episode (boolean list)
 
-The code that uses this data is on GitHub: https://github.com/NathanGavenski/How-resilient-IL-methods-are
+## Citation Information
+```
+@inproceedings{gavenski2022how,
+  title={How Resilient are Imitation Learning Methods to Sub-Optimal Experts?},
+  author={Nathan Gavenski and Juarez Monteiro and Adilson Medronha and Rodrigo Barros},
+  booktitle={2022 Brazilian Conference on Intelligent Systems (BRACIS)},
+  year={2022},
+  organization={IEEE}
+}
+```
+
+## Contact:
+- [Nathan Schneider Gavenski]([email protected])
+- [Juarez Monteiro]([email protected])
+- [Adilson Medronha]([email protected])
+- [Rodrigo C. Barros]([email protected])
 
----
-license: mit
----