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Optimally Teaching a Linear Behavior Cloning Agent

arXiv 2311.15399 ↗First posted 26 Nov 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

We study optimal teaching of Linear Behavior Cloning (LBC) learners. In this setup, the teacher can select which states to demonstrate to an LBC learner. The learner maintains a version space of infinite linear hypotheses consistent with the demonstration. The goal of the teacher is to teach a realizable target policy to the learner using minimum number of state demonstrations. This number is known as the Teaching Dimension(TD). We present a teaching algorithm called ``Teach using Iterative Elimination(TIE)" that achieves instance optimal TD. However, we also show that finding optimal teaching set computationally is NP-hard. We further provide an approximation algorithm that guarantees an approximation ratio of log(|A|-1) on the teaching dimension. Finally, we provide experimental results to validate the efficiency and effectiveness of our algorithm.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 26 Nov 2023.

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References · 14

  1. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
  2. 2019Recent Advances in Imitation Learning from Observationcited by 92
  3. 2020Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learningcited by 81
  4. 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applicationscited by 28
  5. 2019Interactive Teaching Algorithms for Inverse Reinforcement Learningcited by 24
  6. 2020Policy Teaching in Reinforcement Learning via Environment Poisoning Attackscited by 24
  7. 2015The Teaching Dimension of Linear Learnerscited by 19
  8. 2018Teaching Inverse Reinforcement Learners via Features and Demonstrationscited by 19
  9. 2013Machine Teaching for Bayesian Learners in the Exponential Familycited by 18
  10. 2019Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraintscited by 18
  11. 2019Preference-Based Batch and Sequential Teaching: Towards a Unified View of Modelscited by 12
  12. 2021Teaching an Active Learner with Contrastive Examplescited by 12
2 more
  1. 2021Iterative Teaching by Label Synthesiscited by 10
  2. 2018Teacher Improves Learning by Selecting a Training Subsetcited by 6

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