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Repeated Inverse Reinforcement Learning

arXiv 1705.05427 ↗First posted 15 May 2017cs.AI cs.LGcited by 29

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Abstract (arXiv)

We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks that it surprises the human by acting suboptimally with respect to how the human would have acted. Each time the human is surprised, the agent is provided a demonstration of the desired behavior by the human. We formalize this problem, including how the sequence of tasks is chosen, in a few different ways and provide some foundational results.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 May 2017.

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Citing papers per year: 2018: 3; 2019: 3; 2020: 2; 2021: 5; 2022: 6; 2023: 3; 2024: 4; 2025: 3.

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  1. 2018Scalable agent alignment via reward modeling: a research directioncited by 447
  2. 2021On the Expressivity of Markov Rewardcited by 71
  3. 2021Identifiability in inverse reinforcement learningcited by 55
  4. 2022Invariance in Policy Optimisation and Partial Identifiability in Reward Learningcited by 46
  5. 2019Interactive Teaching Algorithms for Inverse Reinforcement Learningcited by 24
  6. 2022How to talk so AI will learn: Instructions, descriptions, and autonomycited by 22
  7. 2018Multi-task Maximum Entropy Inverse Reinforcement Learningcited by 20
  8. 2021Contextual Inverse Optimization: Offline and Online Learningcited by 20
  9. 2019Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraintscited by 18
  10. 2020Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimizationcited by 16
  11. 2022Identifiability and generalizability from multiple experts in Inverse Reinforcement Learningcited by 16
  12. 2023Learning Shared Safety Constraints from Multi-task Demonstrationscited by 15
17 more
  1. 2022A Primer on Maximum Causal Entropy Inverse Reinforcement Learningcited by 13
  2. 2019Inverse Reinforcement Learning in Contextual MDPscited by 12
  3. 2023Inverse Decision Modeling: Learning Interpretable Representations of Behaviorcited by 10
  4. 2021Curriculum Design for Teaching via Demonstrations: Theory and Applicationscited by 9
  5. 2021Learning from an Exploring Demonstrator: Optimal Reward Estimation for Banditscited by 8
  6. 2022Linguistic communication as (inverse) reward designcited by 6
  7. 2024Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learningcited by 6
  8. 2025Agency Is Frame-Dependentcited by 5
  9. 2022Environment Design for Inverse Reinforcement Learningcited by 4
  10. 2024Inverse Reinforcement Learning with Sub-optimal Expertscited by 4
  11. 2018Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learningcited by 3
  12. 2025FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functionscited by 3
  13. 2023Eliciting Risk Aversion with Inverse Reinforcement Learning via Interactive Questioningcited by 2
  14. 2024Towards Generalized Inverse Reinforcement Learningcited by 2
  15. 2025The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learningcited by 1
  16. 2020Interaction-limited Inverse Reinforcement Learningcited by 0
  17. 2024Inference of Utilities and Time Preference in Sequential Decision-Makingcited by 0