# Augmented Q Imitation Learning (AQIL)

arXiv: https://arxiv.org/abs/2004.00993 · HTML: https://mlsanity.com/paper/2004.00993/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 31 Mar 2020.

## Placed in

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

## Builds on (most cited first, 4 of 4)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards](https://mlsanity.com/paper/1905.11108/) (2019)
- [Global overview of Imitation Learning](https://mlsanity.com/paper/1801.06503/) (2018)

## Abstract (arXiv)

The study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning. In imitation learning the machine learns by mimicking the behavior of an expert system whereas in reinforcement learning the machine learns via direct environment feedback. Traditional deep reinforcement learning takes a significant time before the machine starts to converge to an optimal policy. This paper proposes Augmented Q-Imitation-Learning, a method by which deep reinforcement learning convergence can be accelerated by applying Q-imitation-learning as the initial training process in traditional Deep Q-learning.