Datasets:
Tasks:
Other
Size Categories:
100B<n<1T
Language Creators:
expert-generated
Annotations Creators:
machine-generated
Source Datasets:
original
License:
metadata
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. 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
obsis 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}
}