NathanGavenski/How-Resilient-are-Imitation-Learning-Methods-to-Sub-Optimal-Experts

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 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
• Juarez Monteiro
• Adilson Medronha
• Rodrigo C. Barros