mlsanityData to 7 Oct 2026

On Computation and Generalization of Generative Adversarial Imitation Learning

arXiv 2001.02792 ↗First posted 9 Jan 2020cs.LG stat.MLcited by 27

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

Abstract (arXiv)

Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies. Different from Reinforcement Learning (RL), GAIL takes advantage of demonstration data by experts (e.g., human), and learns both the policy and reward function of the unknown environment. Despite the significant empirical progresses, the theory behind GAIL is still largely unknown. The major difficulty comes from the underlying temporal dependency of the demonstration data and the minimax computational formulation of GAIL without convex-concave structure. To bridge such a gap between theory and practice, this paper investigates the theoretical properties of GAIL. Specifically, we show: (1) For GAIL with general reward parameterization, the generalization can be guaranteed as long as the class of the reward functions is properly controlled; (2) For GAIL, where the reward is parameterized as a reproducing kernel function, GAIL can be efficiently solved by stochastic first order optimization algorithms, which attain sublinear convergence to a stationary solution. To the best of our knowledge, these are the first results on statistical and computational guarantees of imitation learning with reward/policy function approximation. Numerical experiments are provided to support our analysis.

Key facts. Cited by 27 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 18 of its 65 references that are in the map. First posted on arXiv 9 Jan 2020.

Builds on

18 of 65 references.

Closest ancestors · 1

Same direction, newest first

  1. 2019On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator11 Jan

Other references · 17

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2018Demystifying MMD GANscited by 1,268
  5. 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 716
  6. 2016Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networkscited by 541
  7. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  8. 2017MMD GAN: Towards Deeper Understanding of Moment Matching Networkcited by 251
  9. 2017Generalization and Equilibrium in Generative Adversarial Nets (GANs)cited by 165
  10. 2015On the Equivalence between Kernel Quadrature Rules and Random Feature Expansionscited by 136
  11. 2017Imitating Driver Behavior with Generative Adversarial Networkscited by 92
  12. 2017Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learningcited by 50
5 more
  1. 2018Learning Temporal Point Processes via Reinforcement Learningcited by 40
  2. 2018Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrationscited by 35
  3. 2018On gradient regularizers for MMD GANscited by 28
  4. 2013Optimal Primal-Dual Methods for a Class of Saddle Point Problemscited by 19
  5. 2017SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 2; 2021: 4; 2022: 6; 2023: 7; 2024: 4; 2025: 3; 2026: 1.

27 papers.

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  1. 2022A Survey on Model-based Reinforcement Learningcited by 65
  2. 2022Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guaranteescited by 42
  3. 2023Diffusion Model-Augmented Behavioral Cloningcited by 37
  4. 2021Online Apprenticeship Learningcited by 28
  5. 2023When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learningcited by 26
  6. 2022Proximal Point Imitation Learningcited by 25
  7. 2023Provably Efficient Adversarial Imitation Learning with Unknown Transitionscited by 16
  8. 2022Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysiscited by 13
  9. 2020When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergencecited by 12
  10. 2022Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guaranteescited by 12
  11. 2021Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximationcited by 11
  12. 2023Making Reconstruction-based Method Great Again for Video Anomaly Detectioncited by 10
15 more
  1. 2021Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrationscited by 9
  2. 2023Testing for the Markov Property in Time Series via Deep Conditional Generative Learningcited by 9
  3. 2020Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Ratecited by 7
  4. 2024OLLIE: Imitation Learning from Offline Pretraining to Online Finetuningcited by 6
  5. 2024Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximationcited by 6
  6. 2021Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Criticcited by 5
  7. 2023Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspectivecited by 4
  8. 2022Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Applicationcited by 3
  9. 2024C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theorycited by 3
  10. 2025Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learningcited by 2
  11. 2024Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guaranteescited by 1
  12. 2025Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Explorationcited by 1
  13. 2023Sample-efficient Adversarial Imitation Learningcited by 0
  14. 2025On Generalization and Distributional Update for Mimicking Observations with Adequate Explorationcited by 0
  15. 2026Non-Adversarial Imitation Learning Provably Free of Compounding Errors: The Value Flow Mechanismcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2018Sample-Efficient Imitation Learning via Generative Adversarial Nets
  2. 2020f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning
  3. 2019Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning
  4. 2018Risk-Sensitive Generative Adversarial Imitation Learning
  5. 2026Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
  6. 2024Diffusion-Reward Adversarial Imitation Learning
  7. 2021A Pragmatic Look at Deep Imitation Learning
  8. 2020Addressing reward bias in Adversarial Imitation Learning with neutral reward functions