| # 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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