mlsanityData to 14 Oct 2026

ReIL: A Framework for Reinforced Intervention-based Imitation Learning

arXiv 2203.15390 ↗First posted 29 Mar 2022cs.RO cs.AI cs.LGcited by 3

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

Abstract (arXiv)

Compared to traditional imitation learning methods such as DAgger and DART, intervention-based imitation offers a more convenient and sample efficient data collection process to users. In this paper, we introduce Reinforced Intervention-based Learning (ReIL), a framework consisting of a general intervention-based learning algorithm and a multi-task imitation learning model aimed at enabling non-expert users to train agents in real environments with little supervision or fine tuning. ReIL achieves this with an algorithm that combines the advantages of imitation learning and reinforcement learning and a model capable of concurrently processing demonstrations, past experience, and current observations. Experimental results from real world mobile robot navigation challenges indicate that ReIL learns rapidly from sparse supervisor corrections without suffering deterioration in performance that is characteristic of supervised learning-based methods such as HG-Dagger and IWR. The results also demonstrate that in contrast to other intervention-based methods such as IARL and EGPO, ReIL can utilize an arbitrary reward function for training without any additional heuristics.

Key facts. Cited by 3 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 15 of its 20 references that are in the map. First posted on arXiv 29 Mar 2022.

Builds on

15 of 20 references.

Closest ancestors · 1

Same direction, newest first

  1. 2018HG-DAgger: Interactive Imitation Learning with Human Experts5 Oct

Other references · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  5. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  6. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  7. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  8. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
  9. 2018Learning to Drive in a Daycited by 273
  10. 2017DART: Noise Injection for Robust Imitation Learningcited by 191
  11. 2018DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skillscited by 163
  12. 2020Human-in-the-Loop Imitation Learning using Remote Teleoperationcited by 112
2 more
  1. 2021Safe Driving via Expert Guided Policy Optimizationcited by 31
  2. 2018Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigationcited by 13

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2023Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasetscited by 58
  2. 2023Guarded Policy Optimization with Imperfect Online Demonstrationscited by 10
  3. 2025Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Surveycited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2024RILe: Reinforced Imitation Learning
  2. 2026One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
  3. 2025Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism
  4. 2023RLIF: Interactive Imitation Learning as Reinforcement Learning
  5. 2022STIR2: Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks
  6. 2024Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model
  7. 2025LLM-based Interactive Imitation Learning for Robotic Manipulation
  8. 2020Scalable Multi-Task Imitation Learning with Autonomous Improvement