# Global overview of Imitation Learning

arXiv: https://arxiv.org/abs/1801.06503 · HTML: https://mlsanity.com/paper/1801.06503/

## Key facts

- Cited by 16 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 3 of its 11 references that are in the map.
- First posted on arXiv 19 Jan 2018.

## Placed in

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

## Builds on (most cited first, 3 of 3)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [One-Shot Imitation Learning](https://mlsanity.com/paper/1703.07326/) (2017)
- [Reinforcement and Imitation Learning via Interactive No-Regret Learning](https://mlsanity.com/paper/1406.5979/) (2014)

## Built on by (most cited first, 16 of 16)

- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Strictly Batch Imitation Learning by Energy-based Distribution Matching](https://mlsanity.com/paper/2006.14154/) (2020)
- [Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer](https://mlsanity.com/paper/2306.08204/) (2023)
- [Time-series Generation by Contrastive Imitation](https://mlsanity.com/paper/2311.01388/) (2023)
- [Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning](https://mlsanity.com/paper/1911.00238/) (2019)
- [The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts](https://mlsanity.com/paper/2107.12254/) (2021)
- [Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/2310.20650/) (2023)
- [A Policy for Early Sequence Classification](https://mlsanity.com/paper/2304.03463/) (2023)
- [Generalized Reinforcement Learning for Building Control using Behavioral Cloning](https://mlsanity.com/paper/2104.00123/) (2021)
- [End-to-End Imitation Learning for Optimal Asteroid Proximity Operations](https://mlsanity.com/paper/2502.01034/) (2025)
- [A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach](https://mlsanity.com/paper/2512.11944/) (2025)
- [Transfer Learning for Prosthetics Using Imitation Learning](https://mlsanity.com/paper/1901.04772/) (2019)
- [Augmented Q Imitation Learning (AQIL)](https://mlsanity.com/paper/2004.00993/) (2020)
- [SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning](https://mlsanity.com/paper/2411.17293/) (2024)
- [Medical Vision Language Models as Policies for Robotic Surgery](https://mlsanity.com/paper/2510.06064/) (2025)
- [Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance](https://mlsanity.com/paper/2605.15012/) (2026)

## Abstract (arXiv)

Imitation Learning is a sequential task where the learner tries to mimic an expert's action in order to achieve the best performance. Several algorithms have been proposed recently for this task. In this project, we aim at proposing a wide review of these algorithms, presenting their main features and comparing them on their performance and their regret bounds.