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

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Tags:
Imitation Learning
Expert Trajectories
Classic Control
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add experts and edit readme.md

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Files changed (21) hide show
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  2. Experts/expert_Acrobot-v1_criteria100.npz +3 -0
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  7. Experts/expert_CartPole-v1_criteria100.npz +3 -0
  8. Experts/expert_CartPole-v1_criteria200.npz +3 -0
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  19. Experts/expert_MountainCar-v0_criteria150.npz +3 -0
  20. Experts/expert_MountainCar-v0_criteria160.npz +3 -0
  21. README.md +14 -0
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  ---
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  license: mit
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  ---
 
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+ # How Resilient are Imitation Learning Methods to Sub-Optimal Experts?
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+ Trajectories used in [How Resilient are Imitation Learning Methods to Sub-Optimal Experts?]()
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+
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+ These trajectories are formed by using [Stable Baselines](https://stable-baselines.readthedocs.io/en/master/).
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+ Each file is a dict of a set of trajectories with the following keys:
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+
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+ * actions: the action in the given timestemp `t`
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+ * obs: current state in the given timestemp `t`
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+ * rewards: reward retrieved after the action in the given timestemp `t`
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+ * episode_returns: The aggregated reward of each episode (each file consists of 5000 runs)
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+ * episode_Starts: Whether that `obs` is the first state of an episode (boolean list).
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+
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+ The code that uses this data is on GitHub: https://github.com/NathanGavenski/How-resilient-IL-methods-are
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+
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  ---
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  license: mit
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  ---