mlsanityData to 7 Oct 2026

Reinforcement and Imitation Learning via Interactive No-Regret Learning

arXiv 1406.5979 ↗First posted 23 Jun 2014cs.LG stat.MLcited by 116

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Cited by 62 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested on can be naturally addressed by an interactive approach and analyzed using no-regret online learning. These approaches to imitation learning, however, neither require nor benefit from information about the cost of actions. We extend existing results in two directions: first, we develop an interactive imitation learning approach that leverages cost information; second, we extend the technique to address reinforcement learning. The results provide theoretical support to the commonly observed successes of online approximate policy iteration. Our approach suggests a broad new family of algorithms and provides a unifying view of existing techniques for imitation and reinforcement learning.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2014.

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Citing papers per year: 2018: 9; 2019: 6; 2020: 5; 2021: 13; 2022: 14; 2023: 20; 2024: 16; 2025: 17; 2026: 16.

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  1. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 517
  2. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  3. 2018An Algorithmic Perspective on Imitation Learningcited by 455
  4. 2024Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoningcited by 267
  5. 2024Trial and Error: Exploration-Based Trajectory Optimization for LLM Agentscited by 194
  6. 2024The pitfalls of next-token predictioncited by 156
  7. 2022Offline Reinforcement Learning with Realizability and Single-policy Concentrabilitycited by 122
  8. 2019Imitation Learning as f-Divergence Minimizationcited by 121
  9. 2019Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learningcited by 100
  10. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 98
  11. 2020Toward the Fundamental Limits of Imitation Learningcited by 96
  12. 2025Process Reward Models for LLM Agents: Practical Framework and Directionscited by 81
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  1. 2021Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gapcited by 80
  2. 2019Provably Efficient Imitation Learning from Observation Alonecited by 78
  3. 2018Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Interventioncited by 77
  4. 2024REBEL: Reinforcement Learning via Regressing Relative Rewardscited by 66
  5. 2023Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorldcited by 65
  6. 2021Imitation Learning: Progress, Taxonomies and Challengescited by 56
  7. 2023Learning to Generate Better Than Your LLMcited by 49
  8. 2020Provable Representation Learning for Imitation Learning via Bi-level Optimizationcited by 41
  9. 2018Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learningcited by 39
  10. 2024Hybrid Inverse Reinforcement Learningcited by 31
  11. 2022Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangiancited by 29
  12. 2024Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHFcited by 29
  13. 2022Causal Imitation Learning under Temporally Correlated Noisecited by 28
  14. 2025Multi-Turn Code Generation Through Single-Step Rewardscited by 27
  15. 2021Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coveragecited by 26
  16. 2022Hindsight Learning for MDPs with Exogenous Inputscited by 25
  17. 2023RLIF: Interactive Imitation Learning as Reinforcement Learningcited by 24
  18. 2019Learning Belief Representations for Imitation Learning in POMDPscited by 23
  19. 2025Accelerating RL for LLM Reasoning with Optimal Advantage Regressioncited by 23
  20. 2023Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Trainingcited by 18
  21. 2018Global overview of Imitation Learningcited by 16
  22. 2021Object-Aware Regularization for Addressing Causal Confusion in Imitation Learningcited by 16
  23. 2023Blending Imitation and Reinforcement Learning for Robust Policy Improvementcited by 16
  24. 2024Diffusing States and Matching Scores: A New Framework for Imitation Learningcited by 16
  25. 2025A Smooth Sea Never Made a Skilled SAILOR: Robust Imitation via Learning to Searchcited by 16
  26. 2025Qsharp: Provably Optimal Distributional RL for LLM Post-Trainingcited by 14
  27. 2021Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimallycited by 12
  28. 2023The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithmscited by 12
  29. 2023Selective Sampling and Imitation Learning via Online Regressioncited by 12
  30. 2025Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecificationcited by 12
  31. 2018OIL: Observational Imitation Learningcited by 11
  32. 2023Active Policy Improvement from Multiple Black-box Oraclescited by 11
  33. 2022Robust Imitation Learning from Corrupted Demonstrationscited by 10
  34. 2023Guarded Policy Optimization with Imperfect Online Demonstrationscited by 10
  35. 2025Formalizing Learning from Language Feedback with Provable Guaranteescited by 10
  36. 2026Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learningcited by 10
  37. 2018Learning Beam Search Policies via Imitation Learningcited by 9
  38. 2026BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Drivingcited by 9

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