mlsanityData to 14 Oct 2026

Hindsight Generative Adversarial Imitation Learning

arXiv 1903.07854 ↗First posted 19 Mar 2019cs.LG stat.MLcited by 3

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

Abstract (arXiv)

Compared to reinforcement learning, imitation learning (IL) is a powerful paradigm for training agents to learn control policies efficiently from expert demonstrations. However, in most cases, obtaining demonstration data is costly and laborious, which poses a significant challenge in some scenarios. A promising alternative is to train agent learning skills via imitation learning without expert demonstrations, which, to some extent, would extremely expand imitation learning areas. To achieve such expectation, in this paper, we propose Hindsight Generative Adversarial Imitation Learning (HGAIL) algorithm, with the aim of achieving imitation learning satisfying no need of demonstrations. Combining hindsight idea with the generative adversarial imitation learning (GAIL) framework, we realize implementing imitation learning successfully in cases of expert demonstration data are not available. Experiments show that the proposed method can train policies showing comparable performance to current imitation learning methods. Further more, HGAIL essentially endows curriculum learning mechanism which is critical for learning policies.

Key facts. Cited by 3 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 26 of its 47 references that are in the map. First posted on arXiv 19 Mar 2019.

Builds on

26 of 47 references.

Closest ancestors · 4

Same direction, newest first

  1. 2018Generative Adversarial Self-Imitation Learning3 Dec
  2. 2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow1 Oct
  3. 2018Generative Adversarial Imitation from Observation17 Jul
  4. 2018Learning Self-Imitating Diverse Policies25 May

Other references · 22

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2016Generative Adversarial Imitation Learningcited by 1,597
  5. 2017Hindsight Experience Replaycited by 1,136
  6. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  7. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
  8. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
  9. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 289
  10. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 260
  11. 2017One-Shot Imitation Learningcited by 233
  12. 2017One-Shot Visual Imitation Learning via Meta-Learningcited by 228
10 more
  1. 2018One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learningcited by 148
  2. 2018Reinforcement and Imitation Learning for Diverse Visuomotor Skillscited by 126
  3. 2016Mode Regularized Generative Adversarial Networkscited by 123
  4. 2018Zero-Shot Visual Imitationcited by 117
  5. 2017Robust Imitation of Diverse Behaviorscited by 86
  6. 2017Neural Task Programming: Learning to Generalize Across Hierarchical Taskscited by 76
  7. 2018Adversarial Imitation via Variational Inverse Reinforcement Learningcited by 36
  8. 2019Curiosity-Driven Experience Prioritization via Density Estimationcited by 27
  9. 2018Energy-Based Hindsight Experience Prioritizationcited by 21
  10. 2018ARCHER: Aggressive Rewards to Counter bias in Hindsight Experience Replaycited by 9

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Cited by year

Citing papers per year: 2020: 1; 2021: 0; 2022: 0; 2023: 1; 2024: 0; 2025: 1.

3 papers.

  1. 2025Dexterous Manipulation through Imitation Learning: A Surveycited by 21
  2. 2020Episodic Self-Imitation Learning with Hindsightcited by 10
  3. 2023Genetic Imitation Learning by Reward Extrapolationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2021Adversarial Option-Aware Hierarchical Imitation Learning
  2. 2020f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning
  3. 2020On Computation and Generalization of Generative Adversarial Imitation Learning
  4. 2018Sample-Efficient Imitation Learning via Generative Adversarial Nets
  5. 2025Goal-based Self-Adaptive Generative Adversarial Imitation Learning (Goal-SAGAIL) for Multi-goal Robotic Manipulation Tasks
  6. 2023Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective
  7. 2022Robust Learning from Observation with Model Misspecification
  8. 2021A Pragmatic Look at Deep Imitation Learning