mlsanityData to 7 Oct 2026

f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning

arXiv 2010.01207 ↗First posted 2 Oct 2020cs.LG cs.AI stat.MLcited by 20

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

Abstract (arXiv)

Imitation learning (IL) aims to learn a policy from expert demonstrations that minimizes the discrepancy between the learner and expert behaviors. Various imitation learning algorithms have been proposed with different pre-determined divergences to quantify the discrepancy. This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency? In this work, we propose f-GAIL, a new generative adversarial imitation learning (GAIL) model, that automatically learns a discrepancy measure from the f-divergence family as well as a policy capable of producing expert-like behaviors. Compared with IL baselines with various predefined divergence measures, f-GAIL learns better policies with higher data efficiency in six physics-based control tasks.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 15 of its 32 references that are in the map. First posted on arXiv 2 Oct 2020.

Builds on

15 of 32 references.

Closest ancestors · 4

Same direction, newest first

  1. 2020Reparameterized Variational Divergence Minimization for Stable Imitation18 Jun
  2. 2020Augmenting GAIL with BC for sample efficient imitation learning21 Jan
  3. 2019A Divergence Minimization Perspective on Imitation Learning Methods6 Nov
  4. 2019Imitation Learning as f-Divergence Minimization30 May

Other references · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  4. 2016Generative Adversarial Imitation Learningcited by 1,576
  5. 2017Wasserstein GANcited by 635
  6. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 504
  7. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  8. 2016Input Convex Neural Networkscited by 407
  9. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  10. 2020Training Deep Energy-Based Models with f-Divergence Minimizationcited by 29
  11. 2018Wasserstein Variational Inferencecited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 7; 2024: 2; 2025: 2; 2026: 1.

20 papers.

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  1. 2022Planning for Sample Efficient Imitation Learningcited by 20
  2. 2023DiffAIL: Diffusion Adversarial Imitation Learningcited by 18
  3. 2024Diffusing States and Matching Scores: A New Framework for Imitation Learningcited by 16
  4. 2023Internally Rewarded Reinforcement Learningcited by 14
  5. 2021Inverse Contextual Bandits: Learning How Behavior Evolves over Timecited by 12
  6. 2023Inverse Decision Modeling: Learning Interpretable Representations of Behaviorcited by 10
  7. 2023Time-series Generation by Contrastive Imitationcited by 8
  8. 2022Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptorscited by 6
  9. 2021Faster Algorithms for Learning Convex Functionscited by 5
  10. 2022ARC - Actor Residual Critic for Adversarial Imitation Learningcited by 3
  11. 2023R2-Diff: Denoising by diffusion as a refinement of retrieved motion for image-based motion predictioncited by 3
  12. 2021Off-policy Imitation Learning from Visual Inputscited by 2
8 more
  1. 2025A Unified Framework for Diffusion Model Unlearning with f-Divergencecited by 2
  2. 2022Auto-Encoding Adversarial Imitation Learningcited by 1
  3. 2023Quantum Imitation Learningcited by 1
  4. 2024PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimizationcited by 1
  5. 2025On Discovering Algorithms for Adversarial Imitation Learningcited by 1
  6. 2022Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspectivecited by 0
  7. 2023Sample-efficient Adversarial Imitation Learningcited by 0
  8. 2026Long-Horizon Language Model Reinforcement Learning via Progressive Point Matchingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective
  2. 2020On Computation and Generalization of Generative Adversarial Imitation Learning
  3. 2020When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence
  4. 2020Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate
  5. 2024C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
  6. 2018Sample-Efficient Imitation Learning via Generative Adversarial Nets
  7. 2021Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
  8. 2024Diffusion-Reward Adversarial Imitation Learning