The Past and Present of Imitation Learning: A Citation Chain Study
On the map: Offline RL and imitation learning from data → Imitation learning methods
Abstract (arXiv)
Imitation Learning is a promising area of active research. Over the last 30 years, Imitation Learning has advanced significantly and been used to solve difficult tasks ranging from Autonomous Driving to playing Atari games. In the course of this development, different methods for performing Imitation Learning have fallen into and out of favor. In this paper, I explore the development of these different methods and attempt to examine how the field has progressed. I focus my analysis on surveying 4 landmark papers that sequentially build upon each other to develop increasingly impressive Imitation Learning methods.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 4 of its 13 references that are in the map. First posted on arXiv 8 Jan 2020.
Builds on
4 of 13 references.
Closest ancestors · 1
Same direction, newest first
Other references · 3
- 2016Generative Adversarial Imitation Learningcited by 1,576
- 2016End to End Learning for Self-Driving Carscited by 1,000
- 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
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Similar, not cited
Close in wording, no citation either way
- 2021Imitation Learning: Progress, Taxonomies and Challenges
- 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
- 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
- 2024A Survey of Imitation Learning Methods, Environments and Metrics
- 2022Interactive Imitation Learning in Robotics: A Survey
- 2019Recent Advances in Imitation Learning from Observation
- 2022Interactive Imitation Learning in Robotics based on Simulations
- 2018An Algorithmic Perspective on Imitation Learning