# Compressed imitation learning

arXiv: https://arxiv.org/abs/2009.11697 · HTML: https://mlsanity.com/paper/2009.11697/

## Key facts

- Cited by 1 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 0 of its 6 references that are in the map.
- First posted on arXiv 18 Sep 2020.

## Placed in

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

## Built on by (most cited first, 1 of 1)

- [On Combining Expert Demonstrations in Imitation Learning via Optimal Transport](https://mlsanity.com/paper/2307.10810/) (2023)

## Abstract (arXiv)

In analogy to compressed sensing, which allows sample-efficient signal reconstruction given prior knowledge of its sparsity in frequency domain, we propose to utilize policy simplicity (Occam's Razor) as a prior to enable sample-efficient imitation learning. We first demonstrated the feasibility of this scheme on linear case where state-value function can be sampled directly. We also extended the scheme to scenarios where only actions are visible and scenarios where the policy is obtained from nonlinear network. The method is benchmarked against behavior cloning and results in significantly higher scores with limited expert demonstrations.