mlsanityData to 14 Oct 2026

A Survey of Imitation Learning Methods, Environments and Metrics

arXiv 2404.19456 ↗First posted 30 Apr 2024cs.LG cs.AIcited by 16

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

Abstract (arXiv)

Imitation learning is an approach in which an agent learns how to execute a task by trying to mimic how one or more teachers perform it. This learning approach offers a compromise between the time it takes to learn a new task and the effort needed to collect teacher samples for the agent. It achieves this by balancing learning from the teacher, who has some information on how to perform the task, and deviating from their examples when necessary, such as states not present in the teacher samples. Consequently, the field of imitation learning has received much attention from researchers in recent years, resulting in many new methods and applications. However, with this increase in published work and past surveys focusing mainly on methodology, a lack of standardisation became more prominent in the field. This non-standardisation is evident in the use of environments, which appear in no more than two works, and evaluation processes, such as qualitative analysis, that have become rare in current literature. In this survey, we systematically review current imitation learning literature and present our findings by (i) classifying imitation learning techniques, environments and metrics by introducing novel taxonomies; (ii) reflecting on main problems from the literature; and (iii) presenting challenges and future directions for researchers.

Key facts. Cited by 16 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 63 of its 112 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

63 of 112 references.

Closest ancestors · 18

Same direction, newest first

  1. 2022Planning for Sample Efficient Imitation Learning18 Oct
  2. 2022Improved Policy Optimization for Online Imitation Learning29 Jul
  3. 2022Imitation Learning from Observations under Transition Model Disparity25 Apr
  4. 2022Conditional Imitation Learning for Multi-Agent Games5 Jan
  5. 2021Cross-domain Imitation from Observations20 May
  6. 2021DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation31 Mar
  7. 2021Off-Policy Imitation Learning from Observations25 Feb
  8. 2021MobILE: Model-Based Imitation Learning From Observation Alone22 Feb
  9. 2020Imitating Unknown Policies via Exploration13 Aug
  10. 2020Intrinsic Reward Driven Imitation Learning via Generative Model26 Jun
  11. 2019Recent Advances in Imitation Learning from Observation30 May
  12. 2019Provably Efficient Imitation Learning from Observation Alone27 May
6 more
  1. 2019Imitation Learning from Video by Leveraging Proprioception22 May
  2. 2019Hybrid Reinforcement Learning with Expert State Sequences11 Mar
  3. 2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow1 Oct
  4. 2018Generative Adversarial Imitation from Observation17 Jul
  5. 2018Imitating Latent Policies from Observation21 May
  6. 2018Behavioral Cloning from Observation4 May

Other references · 45

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  2. 2017Deep reinforcement learning from human preferencescited by 3,912
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  5. 2016Generative Adversarial Imitation Learningcited by 1,597
  6. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  7. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  8. 2018Sanity Checks for Saliency Mapscited by 1,156
  9. 2018DeepMind Control Suitecited by 956
  10. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  11. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 478
  12. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
33 more
  1. 2019Learning by Cheatingcited by 383
  2. 2020dm_control: Software and Tasks for Continuous Controlcited by 375
  3. 2018Quantifying Generalization in Reinforcement Learningcited by 352
  4. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 349
  5. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
  6. 2017The (Un)reliability of saliency methodscited by 333
  7. 2019Learning Latent Plans from Playcited by 316
  8. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 289
  9. 2017Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agentscited by 269
  10. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 263
  11. 2017One-Shot Imitation Learningcited by 233
  12. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 197
  13. 2016What do different evaluation metrics tell us about saliency models?cited by 185
  14. 2016ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learningcited by 179
  15. 2017Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translationcited by 167
  16. 2018Zero-Shot Visual Imitationcited by 117
  17. 2017Third-Person Imitation Learningcited by 103
  18. 2018Playing hard exploration games by watching YouTubecited by 99
  19. 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
  20. 2017Robust Imitation of Diverse Behaviorscited by 86
  21. 2022CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learningcited by 71
  22. 2021panda-gym: Open-source goal-conditioned environments for robotic learningcited by 59
  23. 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
  24. 2023PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordinationcited by 25
  25. 2021Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learningcited by 23
  26. 2019RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstrationcited by 19
  27. 2016Learning Transferable Policies for Monocular Reactive MAV Controlcited by 18
  28. 2019An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generationcited by 11
  29. 2020PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Drivingcited by 11
  30. 2020robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robotscited by 9
  31. 2020Augmented Behavioral Cloning from Observationcited by 5
  32. 2023Repairing Deep Neural Networks Based on Behavior Imitationcited by 4
  33. 2020Inverse Dynamics vs. Forward Dynamics in Direct Transcription Formulations for Trajectory Optimizationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 14; 2025: 1; 2026: 1.

16 papers.

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  1. 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Tokencited by 125
  2. 2024SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Datasetcited by 35
  3. 2024Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Modelscited by 33
  4. 2024Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Modelcited by 16
  5. 2024OLLIE: Imitation Learning from Offline Pretraining to Online Finetuningcited by 6
  6. 2024Explorative Imitation Learning: A Path Signature Approach for Continuous Environmentscited by 3
  7. 2024Conditional Latent ODEs for Motion Prediction in Autonomous Drivingcited by 2
  8. 2024A Bayesian Solution To The Imitation Gapcited by 2
  9. 2024Offline Reinforcement Learning with Imputed Rewardscited by 2
  10. 2024Adaptive Teaching in Heterogeneous Agents: Balancing Surprise in Sparse Reward Scenarioscited by 1
  11. 2024Enabling robots to follow abstract instructions and complete complex dynamic taskscited by 1
  12. 2025Improving Robustness to Out-of-Distribution States in Imitation Learning via Deep Koopman-Boosted Diffusion Policycited by 1
4 more
  1. 2024RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learningcited by 0
  2. 2024Enhancing Reinforcement Learning Through Guided Searchcited by 0
  3. 2024AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systemscited by 0
  4. 2026Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Imitation Learning: Progress, Taxonomies and Challenges
  2. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
  3. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
  4. 2018An Algorithmic Perspective on Imitation Learning
  5. 2022Interactive Imitation Learning in Robotics: A Survey
  6. 2025Learning from Observation: A Survey of Recent Advances
  7. 2020Interactive Imitation Learning in State-Space
  8. 2024VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections