# Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking

arXiv: https://arxiv.org/abs/2403.00550 · HTML: https://mlsanity.com/paper/2403.00550/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 2 of 2)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Imitation Learning: Progress, Taxonomies and Challenges](https://mlsanity.com/paper/2106.12177/) (2021)

## Built on by (most cited first, 3 of 3)

- [Explorative Imitation Learning: A Path Signature Approach for Continuous Environments](https://mlsanity.com/paper/2407.04856/) (2024)
- [Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search](https://mlsanity.com/paper/2605.04057/) (2026)
- [Quantifying Generalisation in Imitation Learning](https://mlsanity.com/paper/2509.24784/) (2025)

## Abstract (arXiv)

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