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Interactively Teaching an Inverse Reinforcement Learner with Limited Feedback

arXiv 2309.09095 ↗First posted 16 Sep 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

We study the problem of teaching via demonstrations in sequential decision-making tasks. In particular, we focus on the situation when the teacher has no access to the learner's model and policy, and the feedback from the learner is limited to trajectories that start from states selected by the teacher. The necessity to select the starting states and infer the learner's policy creates an opportunity for using the methods of inverse reinforcement learning and active learning by the teacher. In this work, we formalize the teaching process with limited feedback and propose an algorithm that solves this teaching problem. The algorithm uses a modified version of the active value-at-risk method to select the starting states, a modified maximum causal entropy algorithm to infer the policy, and the difficulty score ratio method to choose the teaching demonstrations. We test the algorithm in a synthetic car driving environment and conclude that the proposed algorithm is an effective solution when the learner's feedback is limited.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 16 Sep 2023.

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7 of 18 references.

References · 7

  1. 2018An Overview of Machine Teachingcited by 63
  2. 2017Iterative Machine Teachingcited by 41
  3. 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applicationscited by 28
  4. 2019Risk-Aware Active Inverse Reinforcement Learningcited by 26
  5. 2019Interactive Teaching Algorithms for Inverse Reinforcement Learningcited by 24
  6. 2017Towards Black-box Iterative Machine Teachingcited by 20
  7. 2021Curriculum Design for Teaching via Demonstrations: Theory and Applicationscited by 9

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  1. 2018Teaching Inverse Reinforcement Learners via Features and Demonstrations
  2. 2019Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
  3. 2025PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning
  4. 2020Interaction-limited Inverse Reinforcement Learning
  5. 2021Task-Guided Inverse Reinforcement Learning Under Partial Information
  6. 2022Lifelong Inverse Reinforcement Learning
  7. 2020Inverse Reinforcement Learning from a Gradient-based Learner
  8. 2019Improving Interactive Reinforcement Agent Planning with Human Demonstration