|
--- |
|
annotations_creators: |
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- machine-generated |
|
language_creators: |
|
- expert-generated |
|
language: [] |
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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 |
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- Expert Trajectories |
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- Classic Control |
|
--- |
|
|
|
# How Resilient are Imitation Learning Methods to Sub-Optimal Experts? |
|
|
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## Related Work |
|
Trajectories used in [How Resilient are Imitation Learning Methods to Sub-Optimal Experts?]() |
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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` |
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* 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) |
|
|
|
|
