mlsanityData to 7 Oct 2026

Augmented Q Imitation Learning (AQIL)

arXiv 2004.00993 ↗First posted 31 Mar 2020cs.LG cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

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.

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 6 references that are in the map. First posted on arXiv 31 Mar 2020.

Builds on

4 of 6 references.

Closest ancestors · 2

Same direction, newest first

  1. 2019SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards27 May
  2. 2018Global overview of Imitation Learning19 Jan

Other references · 2

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 659

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Similar, not cited

Close in wording, no citation either way

  1. 2021IQ-Learn: Inverse soft-Q Learning for Imitation
  2. 2023Quantum Imitation Learning
  3. 2024Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator
  4. 2021Quantile Filtered Imitation Learning
  5. 2024UNIQ: Offline Inverse Q-learning for Avoiding Undesirable Demonstrations
  6. 2026Q-based Variational Inverse Reinforcement Learning
  7. 2022Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble
  8. 2025Going Beyond Expert Performance via Deep Implicit Imitation Reinforcement Learning