mlsanityData to 7 Oct 2026

imitation: Clean Imitation Learning Implementations

arXiv 2211.11972 ↗First posted 22 Nov 2022cs.LG cs.AIcited by 34

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

Abstract (arXiv)

imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imitation learning algorithms and a preference comparison algorithm. The implementations have been benchmarked against previous results, and automated tests cover 98% of the code. Moreover, the algorithms are implemented in a modular fashion, making it simple to develop novel algorithms in the framework. Our source code, including documentation and examples, is available at https://github.com/HumanCompatibleAI/imitation

Key facts. Cited by 34 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 6 of its 15 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

6 of 15 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021What Matters for Adversarial Imitation Learning?1 Jun
  2. 2018Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning9 Sep

Other references · 4

  1. 2017Deep reinforcement learning from human preferencescited by 3,846
  2. 2016Generative Adversarial Imitation Learningcited by 1,576
  3. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  4. 2021APReL: A Library for Active Preference-based Reward Learning Algorithmscited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 10; 2025: 13; 2026: 3.

34 papers.

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  1. 2023A Survey of Reinforcement Learning from Human Feedbackcited by 259
  2. 2024Human-compatible driving partners through data-regularized self-play reinforcement learningcited by 17
  3. 2023Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competitioncited by 15
  4. 2023RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedbackcited by 15
  5. 2024Diffusion Imitation from Observationcited by 12
  6. 2025CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Modelscited by 11
  7. 2023Residual Q-Learning: Offline and Online Policy Customization without Valuecited by 9
  8. 2025X-IL: Exploring the Design Space of Imitation Learning Policiescited by 9
  9. 2025Reward Learning from Multiple Feedback Typescited by 9
  10. 2023BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Taskscited by 8
  11. 2024RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanationcited by 7
  12. 2025SPACeR: Self-Play Anchoring with Centralized Reference Modelscited by 7
22 more
  1. 2025WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategiescited by 4
  2. 2025Interactive Groupwise Comparison for Reinforcement Learning from Human Feedbackcited by 4
  3. 2022Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robotscited by 3
  4. 2024C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theorycited by 3
  5. 2024Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shapingcited by 3
  6. 2025Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environmentscited by 3
  7. 2024"Give Me an Example Like This": Episodic Active Reinforcement Learning from Demonstrationscited by 2
  8. 2024Synergistic Reinforcement and Imitation Learning for Vision-driven Autonomous Flight of UAV Along Rivercited by 1
  9. 2024Preserving the Privacy of Reward Functions in MDPs through Deceptioncited by 1
  10. 2024A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobilitycited by 1
  11. 2025On Discovering Algorithms for Adversarial Imitation Learningcited by 1
  12. 2023Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learningcited by 0
  13. 2023Dynamic value alignment through preference aggregation of multiple objectivescited by 0
  14. 2024Multi Task Inverse Reinforcement Learning for Common Sense Rewardcited by 0
  15. 2025Supervised Reward Inferencecited by 0
  16. 2025Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamicscited by 0
  17. 2025Learning Dolly-In Filming From Demonstration Using a Ground-Based Robotcited by 0
  18. 2025Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Makingcited by 0
  19. 2025An Introduction to Deep Reinforcement and Imitation Learningcited by 0
  20. 2026Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policiescited by 0
  21. 2026Latent Wasserstein Adversarial Imitation Learningcited by 0
  22. 2026Hierarchical Apprenticeship Learning from Imperfect Demonstrations with Evolving Rewardscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
  2. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
  3. 2022A Ranking Game for Imitation Learning
  4. 2019A Divergence Minimization Perspective on Imitation Learning Methods
  5. 2023Inverse Reinforcement Learning without Reinforcement Learning
  6. 2019Imitation Learning via Off-Policy Distribution Matching
  7. 2024EvIL: Evolution Strategies for Generalisable Imitation Learning
  8. 2024Inverse Reinforcement Learning by Estimating Expertise of Demonstrators