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Data · dataset · 2022

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

Listed in Hugging Face Datasets

Description

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: 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… See the full description on the dataset page: huggingface.co/datasets/NathanGavenski/How-Resilient-are-Imitation-Learning-Methods-to-Sub-Optimal-Experts.

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Stated by source
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Provenance · 1 source records, 9 field assertions
SourceKeyLast seenRaw
Hugging Face DatasetsNathanGavenski/How-Resilient-are-Imitation-Learning-Methods-to-Sub-Optimal-Experts11 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · Hugging Faceconnector:huggingface@1.0.0/gated
concepts[field].local:field:computer-science-aimapping · Hugging Faceconnector:huggingface@1.0.0
concepts[task].hf_task:othersource · Hugging Faceconnector:huggingface@1.0.0/tags[task_categories:*]
created_datesource · Hugging Faceconnector:huggingface@1.0.0
descriptionsource · Hugging Faceconnector:huggingface@1.0.0/description
licensesource · Hugging Faceconnector:huggingface@1.0.0/tags[license:*]
publication_datesource · Hugging Faceconnector:huggingface@1.0.0
titlesource · Hugging Faceconnector:huggingface@1.0.0/id
updated_datesource · Hugging Faceconnector:huggingface@1.0.0