# Imitation learning methods

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

## Key facts

- Imitation learning methods: 746 papers on arXiv since 2018 (data through 7 Oct 2026).
- 101 of its 746 mapped papers (14%) cite “An Algorithmic Perspective on Imitation Learning” (2018, arXiv 1811.06711).
- 97 of its 746 mapped papers (13%) cite “Behavioral Cloning from Observation” (2018, arXiv 1805.01954).
- 8 new papers 17 Sep–7 Oct.

## Most cited here

- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018), cited by 101 papers here
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018), cited by 97 papers here
- [Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning](https://mlsanity.com/paper/1809.02925/) (2018), cited by 96 papers here
- [SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards](https://mlsanity.com/paper/1905.11108/) (2019), cited by 72 papers here
- [A Divergence Minimization Perspective on Imitation Learning Methods](https://mlsanity.com/paper/1911.02256/) (2019), cited by 68 papers here
- [Imitation Learning via Off-Policy Distribution Matching](https://mlsanity.com/paper/1912.05032/) (2019), cited by 68 papers here
- [IQ-Learn: Inverse soft-Q Learning for Imitation](https://mlsanity.com/paper/2106.12142/) (2021), cited by 59 papers here
- [Generative Adversarial Imitation from Observation](https://mlsanity.com/paper/1807.06158/) (2018), cited by 56 papers here
- [Imitation Learning as f-Divergence Minimization](https://mlsanity.com/paper/1905.12888/) (2019), cited by 54 papers here
- [Primal Wasserstein Imitation Learning](https://mlsanity.com/paper/2006.04678/) (2020), cited by 51 papers here

## New (17 Sep–7 Oct)

- [Interleaved Projected Gradient Descent for Safe Imitation Learning](https://mlsanity.com/paper/2610.07167/) (2026-10-05)
- [Reward-DAgger: Robot-Gated Interactive Imitation Learning with General-Purpose Progress-Based Reward Models](https://mlsanity.com/paper/2610.04054/) (2026-10-02)
- [Measuring the Stability Assumption Behind Action Chunking](https://mlsanity.com/paper/2610.01626/) (2026-10-01)
- [Disagreement-Regularized Imitation Learning for Image-Based Continuous Control with Gaussian and Beta Policies](https://mlsanity.com/paper/2609.38407/) (2026-09-29)
- [Provable Benefits of Regularization: Fast Rates for Adversarial Imitation Learning](https://mlsanity.com/paper/2609.35698/) (2026-09-29)
- [Beyond Conservatism: Recoverability-Conditioned Exploration for Model-Based Imitation Learning](https://mlsanity.com/paper/2609.33336/) (2026-09-27)
- [The Statistical Benefits of Multiple Responses for Learning from Demonstrations](https://mlsanity.com/paper/2609.33291/) (2026-09-27)
- [Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning](https://mlsanity.com/paper/2609.30462/) (2026-09-24)