mlsanityData to 14 Oct 2026

Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances

arXiv 2511.03565 ↗First posted 5 Nov 2025cs.LG cs.AIcited by 0

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

Abstract (arXiv)

Imitation learning (IL) enables agents to acquire skills by observing and replicating the behavior of one or multiple experts. In recent years, advances in deep learning have significantly expanded the capabilities and scalability of imitation learning across a range of domains, where expert data can range from full state-action trajectories to partial observations or unlabeled sequences. Alongside this growth, novel approaches have emerged, with new methodologies being developed to address longstanding challenges such as generalization, covariate shift, and demonstration quality. In this survey, we review the latest advances in imitation learning research, highlighting recent trends, methodological innovations, and practical applications. We propose a novel taxonomy that is distinct from existing categorizations to better reflect the current state of the IL research stratum and its trends. Throughout the survey, we critically examine the strengths, limitations, and evaluation practices of representative works, and we outline key challenges and open directions for future research.

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 43 of its 79 references that are in the map. First posted on arXiv 5 Nov 2025.

Builds on

43 of 79 references.

Closest ancestors · 9

Same direction, newest first

  1. 2023Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective18 Dec
  2. 2023Imitation Learning from Observation with Automatic Discount Scheduling11 Oct
  3. 2022Planning for Sample Efficient Imitation Learning18 Oct
  4. 2021Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency11 Dec
  5. 2021Imitation Learning: Progress, Taxonomies and Challenges23 Jun
  6. 2020Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets19 May
  7. 2019Recent Advances in Imitation Learning from Observation30 May
  8. 2018Generative Adversarial Imitation from Observation17 Jul
  9. 2018Behavioral Cloning from Observation4 May

Other references · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  5. 2015Continuous control with deep reinforcement learningcited by 3,407
  6. 2016Generative Adversarial Imitation Learningcited by 1,597
  7. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  8. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  9. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 561
  10. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 509
  11. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
  12. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 298
22 more
  1. 2020First return, then explorecited by 266
  2. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 260
  3. 2023MimicPlay: Long-Horizon Imitation Learning by Watching Human Playcited by 223
  4. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 168
  5. 2019Temporal Cycle-Consistency Learningcited by 154
  6. 2020Learning to Generalize Across Long-Horizon Tasks from Human Demonstrationscited by 115
  7. 2023PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNavcited by 93
  8. 2019IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Datacited by 86
  9. 2018TACO: Learning Task Decomposition via Temporal Alignment for Controlcited by 62
  10. 2022Graph Inverse Reinforcement Learning from Diverse Videoscited by 46
  11. 2022Masked Autoencoding for Scalable and Generalizable Decision Makingcited by 45
  12. 2023Inverse Reinforcement Learning without Reinforcement Learningcited by 36
  13. 2021Safe Driving via Expert Guided Policy Optimizationcited by 31
  14. 2020TextGAIL: Generative Adversarial Imitation Learning for Text Generationcited by 28
  15. 2018Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learningcited by 21
  16. 2021Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspectivecited by 21
  17. 2016Learning Transferable Policies for Monocular Reactive MAV Controlcited by 18
  18. 2022Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learningcited by 10
  19. 2023Improving Long-Horizon Imitation Through Instruction Predictioncited by 8
  20. 2024PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learningcited by 8
  21. 2021Improving Sequential Recommendation Consistency with Self-Supervised Imitationcited by 7
  22. 2024Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignmentcited by 3

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  1. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
  2. 2018An Algorithmic Perspective on Imitation Learning
  3. 2024A Survey of Imitation Learning Methods, Environments and Metrics
  4. 2021Imitation by Predicting Observations
  5. 2025An Introduction to Deep Reinforcement and Imitation Learning
  6. 2025Learning from Observation: A Survey of Recent Advances
  7. 2022Imitation Learning by Estimating Expertise of Demonstrators
  8. 2022Diverse Imitation Learning via Self-Organizing Generative Models