  Imitation learning field requires expert data to train agents in a task. Most
often, this learning approach suffers from the absence of available data, which
results in techniques being tested on its dataset. Creating datasets is a
cumbersome process requiring researchers to train expert agents from scratch,
record their interactions and test each benchmark method with newly created
data. Moreover, creating new datasets for each new technique results in a lack
of consistency in the evaluation process since each dataset can drastically
vary in state and action distribution. In response, this work aims to address
these issues by creating Imitation Learning Datasets, a toolkit that allows
for: (i) curated expert policies with multithreaded support for faster dataset
creation; (ii) readily available datasets and techniques with precise
measurements; and (iii) sharing implementations of common imitation learning
techniques. Demonstration link:
https://nathangavenski.github.io/#/il-datasets-video
