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# How Resilient are Imitation Learning Methods to Sub-Optimal Experts?
Trajectories used in [How Resilient are Imitation Learning Methods to Sub-Optimal Experts?]()
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).
The code that uses this data is on GitHub: https://github.com/NathanGavenski/How-resilient-IL-methods-are
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license: mit
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