| --- |
| annotations_creators: |
| - machine-generated |
| language_creators: |
| - expert-generated |
| language: [] |
| license: |
| - mit |
| multilinguality: [] |
| size_categories: |
| - 100B<n<1T |
| source_datasets: |
| - original |
| task_categories: |
| - other |
| task_ids: [] |
| pretty_name: How Resilient are Imitation Learning Methods to Sub-Optimal Experts? |
| tags: |
| - Imitation Learning |
| - Expert Trajectories |
| - Classic Control |
| --- |
| |
| # 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: |
|
|
| * actions: the action in the given timestamp `t` |
| * 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) |
|
|
| ## 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](nathan.gavenski@edu.pucrs.br) |
| - [Juarez Monteiro](juarez.santos@edu.pucrs.br) |
| - [Adilson Medronha](adilson.medronha@edu.pucrs.br) |
| - [Rodrigo C. Barros](rodrigo.barros@pucrs.br) |
|
|
|
|
