
# 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)


task_categories:other annotations_creators:machine-generated language_creators:expert-generated size_categories:100B<n<1T source_datasets:original license:mit Imitation Learning Expert Trajectories Classic Control region:us
{"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"]}
TAGS
#task_categories-other #annotations_creators-machine-generated #language_creators-expert-generated #size_categories-100B<n<1T #source_datasets-original #license-mit #Imitation Learning #Expert Trajectories #Classic Control #region-us 


# 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: URL

# Structure
These trajectories are formed by using Stable Baselines.
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)




## Contact:
- Nathan Schneider Gavenski
- Juarez Monteiro
- Adilson Medronha
- Rodrigo C. Barros


# 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: URL # Structure
These trajectories are formed by using Stable Baselines.
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) ## Contact:
- Nathan Schneider Gavenski
- Juarez Monteiro
- Adilson Medronha
- Rodrigo C. Barros
TAGS
#task_categories-other #annotations_creators-machine-generated #language_creators-expert-generated #size_categories-100B<n<1T #source_datasets-original #license-mit #Imitation Learning #Expert Trajectories #Classic Control #region-us 
 # 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: URL # Structure
These trajectories are formed by using Stable Baselines.
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) ## Contact:
- Nathan Schneider Gavenski
- Juarez Monteiro
- Adilson Medronha
- Rodrigo C. Barros
passage: TAGS
#task_categories-other #annotations_creators-machine-generated #language_creators-expert-generated #size_categories-100B<n<1T #source_datasets-original #license-mit #Imitation Learning #Expert Trajectories #Classic Control #region-us 
# 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: URL# Structure
These trajectories are formed by using Stable Baselines.
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)## Contact:
- Nathan Schneider Gavenski
- Juarez Monteiro
- Adilson Medronha
- Rodrigo C. Barros