mlsanityData to 7 Oct 2026

Interaction-limited Inverse Reinforcement Learning

arXiv 2007.00425 ↗First posted 1 Jul 2020cs.LG stat.MLcited by 0

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher textitinteraction is textitlimited during training. Our setting is motivated by the realistic scenarios where a helpful teacher is not available or when the teacher cannot access the learning dynamics of the student. We present two different training strategies: Curriculum Inverse Reinforcement Learning (CIRL) covering the teacher's perspective, and Self-Paced Inverse Reinforcement Learning (SPIRL) focusing on the learner's perspective. Using experiments in simulations and experiments with a real robot learning a task from a human demonstrator, we show that our training strategies can allow a faster training than a random teacher for CIRL and than a batch learner for SPIRL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 14 of its 38 references that are in the map. First posted on arXiv 1 Jul 2020.

Builds on

14 of 38 references.

Closest ancestors · 2

Same direction, newest first

  1. 2019Interactive Teaching Algorithms for Inverse Reinforcement Learning28 May
  2. 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications20 May

Other references · 12

  1. 2015Scheduled Sampling for Sequence Prediction with Recurrent Neural Networkscited by 779
  2. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 652
  3. 2016Cooperative Inverse Reinforcement Learningcited by 298
  4. 2017Automated Curriculum Learning for Neural Networkscited by 251
  5. 2017Reverse Curriculum Generation for Reinforcement Learningcited by 185
  6. 2014Learning to Executecited by 105
  7. 2018An Overview of Machine Teachingcited by 63
  8. 2016First-Person Activity Forecasting with Online Inverse Reinforcement Learningcited by 40
  9. 2017Iterative Machine Teachingcited by 40
  10. 2018Learning Curriculum Policies for Reinforcement Learningcited by 34
  11. 2018Theory of Curriculum Learning, with Convex Loss Functionscited by 33
  12. 2017Repeated Inverse Reinforcement Learningcited by 29

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Inverse Reinforcement Learning without Reinforcement Learning
  2. 2024Hybrid Inverse Reinforcement Learning
  3. 2018Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
  4. 2019Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
  5. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  6. 2026Interactive Inverse Reinforcement Learning of Interaction Scenarios via Bi-level Optimization
  7. 2023Curricular Subgoals for Inverse Reinforcement Learning
  8. 2020Inverse Reinforcement Learning from a Gradient-based Learner